Semi-supervised learning (SSL) based on pseudo-label and consistency has achieved significant success. The core idea behind these methods is to assign sample weights based on pseudo-label probabilities, thereby guiding the model toward biased learning. However, existing research still faces two major challenges in guiding learning: (1) how to evaluate learning states across different classes in the absence of labels, and (2) how to construct an effective sample weight space that provides precise guidance throughout training. To address these challenges, we propose the Bi-Dimensional Sample Weight Guidance algorithm, BidMatch. BidMatch introduces Class Information Entropy (CIE), which captures the learning relationships between classes and reflects the model’s learning state for each class. Additionally, Pseudo-label Probability Redistribution (PPR) is proposed to maintain distribution invariance and sparsity during training, thereby emphasizing differences in sample importance. By leveraging CIE and PPR, BidMatch generates sample weights that account for both class and instance dimensions, effectively guiding the model toward balanced and efficient learning across classes. BidMatch has demonstrated state-of-the-art performance on various SSL datasets. Notably, it achieved a 6.45% error rate on CIFAR-10 with only one label per class, significantly outperforming baseline methods.

The vulnerability of graph-based recommender systems to spurious correlations has become a significant obstacle to their practical deployment, hindering their robustness in out-of-distribution (OOD) scenarios. While existing approaches offer partial solutions, they are limited by fundamental shortcomings: model-centric approaches reliant on predefined causal graphs often suffer from suboptimal performance due to complex and dynamic environmental influences. These methods typically require identifying an environmental label or performing feature decoupling, but hidden environments are often difficult to model. Furthermore, existing general feature decoupling methods fail to account for the unique structural characteristics of graphs. To overcome these challenges, we advocate for a shift towards explicit, geometrically-grounded disentanglement. Hyperbolic geometry is particularly suited for this task due to its capacity to model the inherent hierarchies of user interests. We introduce C-HyPOD : Causal Hyperbolic Representation Learning with Prototype Orthogonal Disentanglement, a novel framework designed for graph-based OOD recommendation. Unlike traditional methods, C-HyPOD transforms disentanglement into a concrete geometric task. It introduces a global interest space by learning a single set of universal interest prototypes. They provides a superior geometric foundation for ensuring these prototypes are well-separated and semantically distinct. To ensure a complete separation and prevent information leakage, a targeted orthogonality constraint is then applied. This constraint purifies the aggregated causal representation by forcing it to be orthogonal to the spurious representation in the tangent space, thereby eliminating their linear correlation. Extensive experiments on four public datasets demonstrate that C-HyPOD significantly improves OOD robustness and recommendation performance, surpassing state-of-the-art methods.

In Internet of Things (IoT), artificial intelligence (AI) agents have become a core paradigm for edge intelligence, supporting on-device sensor data processing and privacy-preserving analytics. In practice, however, the sensor data streams evolve continuously and often exhibit nonindependent and identically distributed (non-IID) characteristics. Federated class-incremental learning (FCIL) has thus emerged as a promising approach to endow edge AI agents with long-tail learning capabilities, allowing models to adapt autonomously to persistent changes in the data. Despite recent progress, most existing FCIL methods assume balanced data distributions, which rarely hold in real-world deployments. Under realistic long-tailed distributions, the local optimization objectives of individual AI agents diverge from the desired global convergence, leading to head-class bias and rapid forgetting of tail classes. To overcome these limitations, we propose gradient-aligned and imbalance-aware FCIL model (FedGRAIL), a unified FCIL framework that resolves the conflict between local optimization bias and global consistency in dynamic IoT environments. FedGRAIL integrates local training and global aggregation within a structurally aligned learning process, rather than treating them as separate stages. It further introduces a gradient-correction mechanism that coordinates dispersed local optima and stabilizes global optimization via diversity-driven generative replay. This design supports effective retention of minority concepts while respecting the zero-storage constraints of edge agents. Extensive experiments on long-tailed benchmarks show that FedGRAIL substantially improves tail-class accuracy under both independent and identically distributed (IID) and non-IID conditions - achieving a 59% average gain over the strongest baseline, while preserving the competitive accuracy on head classes. These results demonstrate that FedGRAIL provides a storage-efficient, privacy-preserving, and robust solution for continual learning in IoT edge intelligence.

Multimodal Sentiment Analysis (MSA) plays a vital role in understanding emotional content from social media and multimedia data. However, existing methods often rely on large-scale labeled datasets, leading to high annotation costs and poor adaptability. They also suffer from modality imbalance and suboptimal feature fusion. To address these issues, we propose MapleNet—a Multimodal Affect Perception framework enhanced by Large Language Models. MapleNet integrates a prototype-guided fusion strategy and a dynamic modality balancing mechanism to improve alignment and collaboration between text and image features. Specifically, a shared-space encoder combined with prompt optimization ensures semantic consistency across modalities. Within the prototype learning framework, the model dynamically adjusts modalityspecific learning by aligning features with class prototypes, thus mitigating imbalance and uncovering complementary affective cues. In addition, MapleNet employs a similaritybased sample retrieval module to construct contextual prompts, enriching sentiment understanding in few-shot settings. Experiments on six benchmark datasets show that MapleNet consistently outperforms state-of-the-art methods, especially under few-shot conditions, achieving superior accuracy and generalization. The relevant code is available at https://github.com/YFanLuo/MapleNet.

Fatigued driving is a major societal risk factor in the road safety system, affecting the efficiency of the entire transportation network and public safety. Fatigue detection methods based on multimodal physiological signals can objectively reflect a driver’s neurocognitive state, offering higher monitoring reliability. However, existing methods face two main challenges: 1) the high cost of annotating high-quality physiological signals leads to challenges in learning with limited data; and 2) the signal-to-noise ratio of multimodal physiological signals is variable, leading to imbalances in the confidence in the decision between modalities. To address these issues, this article proposes the meta-evidential dual-stream network (MEDS-Net), which integrates a meta-learning framework with evidential deep learning to establish a solution for driver fatigue detection for the first time. Specifically, MEDS-Net employs a dual-stream evidence generation network to extract heterogeneous features from multimodal physiological signals and uses a Dirichlet distribution to quantify cognitive uncertainty for each modality. Additionally, MEDS-Net introduces a meta-learning framework to enhance the model’s ability to handle data scarcity through the design of task simulation mechanisms and a two-layer optimization strategy. Extensive experiments on the SEED-VIG dataset demonstrate that MEDS-Net outperforms current state-of-the-art methods in terms of effectiveness and generalizability. Furthermore, MEDS-Net can precisely quantify the importance of global features in explaining modal contributions and average activation patterns.

Semi-supervised multiview clustering has garnered considerable attention for its ability to integrate multiview data with limited labeled information. However, existing methods predominantly focus on labeled samples, neglecting abundant unlabeled data, which leads to suboptimal utilization of available prior knowledge. Moreover, existing pairwise constraint propagation-based methods typically follow a two-stage procedure, resulting in unstable clustering outcomes. To address these limitations, we introduce a unified framework that integrates multiview subspace clustering with pairwise constraint propagation, proposing a tensor-based semi-supervised multiview subspace clustering (TSMSC) method with pairwise constraint propagation. Specifically, each view's subspace representation is decomposed into a consensus part and private parts, enabling the consensus representation to better approximate the low-rank structure. Then, a pairwise constraint propagation method is developed for multiview data, which propagates the initial pairwise constraint through a low-rank matrix completion approach. Finally, observing that the ideal multiview consensus representation and the propagated pairwise constraint matrix share the same low-rank structure, we naturally construct them into a third-order tensor to capture high-order correlations via tensor low-rank representation, allowing for joint optimization within a unified framework. Extensive experiments on eight real-world datasets demonstrate the superiority of our method over state-of-the-art approaches.

The emergence of large language models (LLMs) has been a key enabler of technological innovation in healthcare. People can conveniently obtain a more accurate medical consultation service by utilizing LLMs' powerful knowledge inference capability. However, existing LLMs require users to upload explicit requests during remote healthcare consultations, which involves the risk of exposing personal privacy. Furthermore, the reliability of the response content generated by LLMs is not guaranteed. To tackle the above challenges, this paper proposes a novel privacy-preserving LLM for user-activated health, called Adaptive Compressed-based Privacy-preserving LLM (ACP2LLM). Specifically, an adaptive token compression method based on information entropy is carefully designed to ensure that ACP2LLM can preserve user-sensitive information when invoking the medical consultation of LLMs deployed on the cloud platform. Moreover, a multi-doctor one-chief physician mechanism is proposed to rationally split and collaboratively infer the patients' requests to achieve the privacy-utility trade-off. Notably, the proposed ACP2LLM also provides highly competitive performance in various token compression rates. Extensive experiments on multiple Medical Question and Answers datasets demonstrate that the proposed ACP2LLM has strong privacy protection capabilities and high answer precision, outperforming current state-of-the-art LLM methods.

Anchor-based multi-view clustering has gained increasing attention for its efficiency in approximating similarity structures and scaling to large datasets. To reduce the burden of manual hyper-parameter tuning, recent studies have introduced parameter-free extensions. However, existing methods still face critical challenges: anchors are typically fixed after initialization, limiting adaptability to heterogeneous data; enforcing a shared anchor set across views suppresses view-specific diversity; and heuristic or self-weighted fusion strategies often lack explicit cross-view alignment, resulting in structural inconsistencies. To address these issues, we propose a Parameter-Free Multi-view Clustering framework with Adaptive Anchors for Large-scale Data (FPMCAA). Unlike existing approaches that decouple anchor construction and graph fusion, FPMCAA integrates adaptive anchor learning, anchor graph construction, and explicit cross-view alignment within a unified optimization model. Anchors are iteratively refined to capture complex distributions, while view-specific graphs are aligned toward a consensus structure without sacrificing inherent diversity. The framework avoids manual hyperparameter tuning and achieves linear computational complexity through efficient alternating optimization. Extensive experiments on benchmark datasets demonstrate that FPMCAA consistently outperforms state-of-the-art methods in clustering performance, robustness, and scalability. The source code of FPMCAA is available athttps://github.com/Xuchen2020/FPMCAA.

Active learning can effectively reduce the cost of labeling while enhancing model classification performance. However, prior studies have indicated that imbalanced class distributions adversely impact active learning, leading to diminished model effectiveness. Existing approaches to unbalanced active learning often neglect the multi-class imbalance problem and suffer from low performance and high time consumption. To address these issues, this paper introduces a hybrid active learning with online weighted broad learning system (HAL-OWBLS). Its main advantages include: (1) We optimize the initial labeled instance selection through an approximate query strategy to avoid the cold-start problem and introduce a sample selection strategy based on double uncertainty to enhance the rationality of active learning iterations. (2) A weighted broad learning system (WBLS) is chosen as the classifier, and an improved weighting strategy is adopted for multi-class imbalanced data. (3) We theoretically derive an efficient online updating model for WBLS, which reduces the time cost of active learning iterations by using only newly labeled samples for fast updating. The proposed HAL-OWBLS algorithm has better performance and robustness compared with existing related algorithms on various multi-class imbalanced data sets.

The challenges associated with large-scale user-item interaction graphs have attracted increasing attention in graph-based recommendation systems, primarily due to computational inefficiencies and inadequate information propagation. Existing methods provide partial solutions but suffer from notable limitations: model-centric approaches, such as sampling and aggregation, often struggle with generalization, while data-centric techniques, including graph sparsification and coarsening, lead to information loss and ineffective handling of bipartite graph structures. Recent advances in graph condensation offer a promising direction by reducing graph size while preserving essential information, presenting a novel approach to mitigating these challenges. Inspired by the principles of democracy, we proposeDemoRec, a framework that leverages graph condensation to generate user and item representatives for recommendation tasks. By constructing a compact interaction graph and clustering nodes with shared characteristics from the original graph, DemoRec significantly reduces graph size and computational complexity. Furthermore, it mitigates the over-reliance on high-order information, a critical challenge in large-scale bipartite graphs. Extensive experiments conducted on four public datasets demonstrate the effectiveness of DemoRec, showcasing substantial improvements in recommendation performance, computational efficiency, and robustness compared to SOTA methods.

The processing of continuous data streams in non-stationary environments has gained increasing attention. However, supervised online learning is often limited by label availability. Furthermore, it is crucial to develop a stable and high-performance online method in non-stationary environments. To tackle these issues, we propose a dynamic chunk-based active learning framework (DCAL). This framework includes a dynamic dual-stage query strategy and an enhanced active learning model. Specifically, the proposed query strategy, referred to as DyDQS, evaluates sample value comprehensively by considering local density, uncertainty, and dynamic imbalance ratio. This approach selects samples that are both representative and uncertain, while also enhancing the likelihood of selecting minority class samples. Additionally, we introduce an enhanced active learning model, named eBLS-W, which is based on the broad learning system (BLS). We redesign the update rule of BLS and equip it with a kernel mapping to improve its robustness and performance, enabling it to better handle non-stationary environments. The effectiveness of the DyDQS, eBLS-W, and DCAL was validated through experiments on synthetic datasets with drift and real-world datasets. The results demonstrate that our approach outperforms other advanced methods in terms of robustness and accuracy.

Active learning can significantly reduce the cost of labeling instances while improving model performance. However, similar to other traditional algorithms, active learning encounters the problem of class imbalance and delivers sub-optimal performance. Additionally, existing approaches suffer from poor performance and are time-consuming. To address these issues, we propose an Actively Incrementally Weighted Broad Learning System (AI-WBLS). Firstly, we introduce an active learning framework based on the weighted broad learning system, which employs a double uncertainty sample selection strategy to enhance the value and reasonableness of sample selection in each iteration of active learning. To further improve the model's adaptability during the iterative learning process, an adaptive weighting strategy is designed to adaptively modify the penalty weights according to the changes in the sample labels. Finally, an efficient incremental paradigm is developed to update the model with newly labelled samples instead of re-training, resulting in improved performance and efficiency. Extensive comparative experiments confirm that our approach outperforms other imbalanced active learning methods.

Multi-view clustering has exhibited exceptional performance by harnessing complementary information and extracting shared semantics across diverse views. However, existing methods still encounter substantial obstacles in exploiting high-quality consensus representation: (i) they typically lack effective guidance mechanisms under unsupervised conditions, leading to suboptimal representation learning, and (ii) they overlook the intrinsic cluster structure when forming sample pairs to align representation across views, conflicting with clustering objectives. To address these issues, we propose a contrastive-based method calledPseudo-LabelSimilarityGraph-driven Multi-ViewClustering (PSGVC). Specifically, to uncover the potential semantic relationships between samples, we construct a pseudo-label similarity graph based on the consensus representation. Then, we design a similarity graph-based contrastive loss, which utilizes the pseudo-label similarity graph as a global supervision signal to guide the optimization of cross-view embedded representation, thereby indirectly improving the quality of consensus representation. Additionally, we propose a weighted cluster-aware contrastive loss to align the consensus representation with view-specific representation. It leverages the cluster structure contained in the pseudo-label similarity graph to achieve cluster-level contrastive learning, further enhancing the quality of the consensus representation. Extensive experimental results show that our PSGVC achieves state-of-the-art clustering performance across multiple datasets.

The contactless interaction approach enables robots to identify object attributes via sensors and advanced perceptual technologies, eliminating the need for physical contact. This approach enhances interaction flexibility and safety while offering a richer, more intuitive human-robot experience. Current research focuses on cross-modal retrieval through non-contact feature extraction, enabling material attribute estimation and advancing object perception. However, existing cross-modal material retrieval methods overlook learning common attributes shared within the same material type. To address this, we propose Materials Perception with Self-paced Prototype contrastive Learning (MPSPL). First, attribute prototype contrastive learning extracts shared characteristics within the same material type, aggregates similar samples, and enhances the perception of deep model material attributes. Second, a self-paced learning strategy dynamically adjusts the contrastive loss temperature coefficient, guiding the model to discern discriminative features across materials and progressively recognize inherent attributes. Finally, extensive experiments validate our method under both known and unknown material category conditions. Experiments demonstrate the superiority of our proposed method over state-of-the-art approaches.

Due to its exceptional feature representation capabilities and high computational efficiency, the broad learning system (BLS) has been widely employed in various classification tasks. Nevertheless, BLS encounters considerable challenges in semi-supervised classification tasks involving complex heterogeneous data, given the data’s high-dimensional and noisy nature, coupled with a limited number of available labeled samples. To tackle these challenges, this article introduces a semi-supervised BLS based on distance constraint regularization (DRBLS) and a semi-supervised broad ensemble method (E-DRBLS) for high-dimensional data. Specifically, we present a distance constraint regularization (DR) that utilizes both labeled and unlabeled data to derive an optimal projection matrix, which maximizes the preservation of the original data’s intrinsic distribution structure. DR is designed to minimize intraclass distance, maximize interclass distance, and minimize the distance between neighboring samples. To boost the performance of BLS in semi-supervised classification, we integrate DR and BLS to construct the semi-supervised classifier DRBLS. Finally, we propose a mixed dimensionality reduction space generation (MDRSG) method that generates multiple high-quality and diverse mixed dimensionality reduction spaces (MDRSs). Based on MDRS, an ensemble framework, E-DRBLS, is developed for semi-supervised classification tasks targeting high-dimensional data. Comprehensive experiments confirm the superiority of the proposed methods.

With the rapid advancement of video generation models such as Sora, video quality assessment (VQA) is becoming increasingly crucial for selecting high-quality videos from large-scale datasets used in pre-training. Traditional VQA methods, typically producing single numerical scores, often lack comprehensiveness and interpretability. To address these challenges, we introduce MVQA-68K, a novel multi-dimensional VQA dataset comprising over 68,000 carefully annotated videos, covering seven essential quality dimensions: overall aesthetics, camera movement, dynamic degree, texture detail, composition, visual quality, and factual consistency. Each annotation includes detailed chain-of-thought reasoning to facilitate interpretability and comprehensive understanding. Extensive experiments demonstrate that MVQA-68K significantly enhances the performance of various multimodal large language models (MLLMs) on the VQA task, achieving state-of-the-art results not only on our internal test set (Fig.1) but also on public benchmarks including LSVQ-test, LSVQ-1080p, and LIVE-VQC. Meantime, incorporating explicit reasoning process during VQA training substantially boosts the zero-shot generalization. Code and dataset will be available at github: https://github.com/Controller01-ai/MVQA-68K.

Abstract Multi-view learning is an emerging field that aims to enhance learning performance by leveraging multiple views or sources of data across various domains. By integrating information from diverse perspectives, multi-view learning methods effectively enhance accuracy, robustness, and generalization capabilities. The existing research on multi-view learning can be broadly categorized into four groups in the survey based on the tasks it encompasses, namely multi-view classification approaches, multi-view semi-supervised classification approaches, multi-view clustering approaches, and multi-view semi-supervised clustering approaches. Despite its potential advantages, multi-view learning poses several challenges, including view inconsistency, view complementarity, optimal view fusion, the curse of dimensionality, scalability, limited labels, and generalization across domains. Nevertheless, these challenges have not discouraged researchers from exploring the potential of multiview learning. It continues to be an active and promising research area, capable of effectively addressing complex real-world problems.

Graph-based methods have demonstrated exceptional performance in semi-supervised classification. However, existing graph-based methods typically construct either a predefined graph in the original space or an adaptive graph within the output space, which often limits their ability to fully utilize prior information and capture the optimal intrinsic data distribution, particularly in high-dimensional data with abundant redundant and noisy features. This paper introduces a novel approach: Semi-Supervised Classification with Optimized Graph Construction (SSC-OGC). SSC-OGC leverages both predefined and adaptive graphs to explore intrinsic data distribution and effectively employ prior information. Additionally, a graph constraint regularization term (GCR) and a collaborative constraint regularization term (CCR) are incorporated to further enhance the quality of the adaptive graph structure and the learned subspace, respectively. To eliminate the negative effect of constructing a predefined graph in the original data space, we further propose a Hybrid Subspace Ensemble-enhanced framework based on the proposed Optimized Graph Construction method (HSE-OGC). Specifically, we construct multiple hybrid subspaces, which consist of meticulously chosen features from the original data to achieve high-quality and diverse space representations. Then, HSE-OGC constructs multiple predefined graphs within hybrid subspaces and trains multiple SSC-OGC classifiers to complement each other, significantly improving the overall performance. Experimental results conducted on various high-dimensional datasets demonstrate that HSE-OGC exhibits outstanding performance.

Physiological multimodal emotion recognition (PMER) has become a key research direction for advancing human-computer interaction and affective computing. However, current PMER methods are affected by significant individual differences and the limited number of samples, making it challenging to capture complex emotional states comprehensively. To address aforementioned issues, this letter proposes a novel multimodal Few-Shot emotion recognition model, called Hierarchical Interactive Alignment Network (HIA-Net). Specifically, the Hierarchical Adaptive Interactive Attention (HAIA) module of HIA-Net is proposed to capture multidimensional emotional features and aggregate the cross-modal information effectively. Additionally, a cross-domain optimization strategy based on the maximum mean discrepancy is proposed to enhance the HIA-Net's adaptability across varying data distributions. Experimental results show that HIA-Net achieves state-of-the-art performance under Few-Shot experimental paradigms on the SEED and SEED-FRA datasets. Our codes are available athttps://github.com/As1yk/HIA-Net.

In the field of online learning, handling imbalanced data streams with concept drift has become a substantial challenge. Research interest in the combined challenges of concept drift and class imbalance is steadily growing. When dealing with concept drift, existing methods usually adopt active detection or passive adaptation to deal with concept drift. However, a single adaptation method is relatively limited in dealing with joint concept drift. This paper introduces a novel hybrid ensemble framework (HEF-IDS) specifically designed to tackle drifting data streams while effectively accommodating class imbalance. The framework integrates an active detection module and a passive adaptation module. In the passive adaptation module, we use a hybrid sampling strategy to balance data chunks, construct a sub-ensemble classifier for each balanced chunk, and combine it with a weighted voting scheme to deal with the class imbalance and adapt to the latest concept. In the process of active detection, we design a new sample selection mechanism that stores the most informative samples in the buffer, and uses these samples for training when drift is detected to prevent catastrophic forgetting. Comparative results on artificial and real-world datasets show that the proposed HEF-IDS is superior to other advanced online classification methods.

As data continues to grow exponentially, the importance of online learning across various domains has increased significantly. However, most existing studies assume that the true class label for each incoming data point is readily available, an assumption that is often impractical. To address this issue, this paper introduces a novel algorithm called Density-based Clustering and One-Class Ensemble Active Learning (DCOE-AL). This approach constructs an ensemble model by combining a clustering algorithm with the One-Class Broad Learning System (OCBLS), which represents clusters within the feature space. Furthermore, a new active learning mechanism is developed to enable DCOE-AL to effectively handle challenges associated with concept drift and label scarcity. The proposed method is evaluated on multiple synthetic data streams that exhibit diverse types of concept drift, as well as on several real-world data streams. Comparative evaluations demonstrate that DCOE-AL achieves superior performance while requiring significantly fewer labeled samples.

Time series anomaly detection is a pivotal task in data analysis, yet it poses the challenge of discerning normal and abnormal patterns in label-deficient scenarios. While prior studies have largely employed reconstruction-based approaches, which limit the models’ representational capacities. Moreover, existing deep learning-based methods are not sufficiently lightweight. Addressing these issues, we present PatchAD, our novel, highly efficient multiscale patch-based MLP-Mixer architecture that utilizes contrastive learning for representation extraction and anomaly detection. With its four distinct MLP Mixers and innovative dual project constraint module, PatchAD mitigates potential model degradation and offers a lightweight solution, requiring only0.403 Mparameters. Its efficacy is demonstrated by state-of-the-art results across8datasets sourced from different application scenarios, outperforming over30comparative algorithms. PatchAD significantly improves the classical F1 score by6.84%, the Aff-F1 score by4.27%, and the V-ROC by2.49%. Simultaneously, an in-depth analysis of the mechanisms underlying PatchAD has been conducted from both theoretical and experimental perspectives, validating the design motivations of the model.

Credit card (CC) fraud detection within the realm of financial security faces challenges such as data imbalance, large-scale anonymized transaction datasets, and the need for system-specific model training. Past methods often fail to address these aforementioned issues simultaneously. Utilizing a single model results in a lack of zero-shot capability without adaptation for real-world scenarios. This article introduces LLM40FD, a novel framework that leverages a large language model (LLM) to overcome these obstacles in anonymous zero-shot fraud detection. LLM40FD addresses the aforementioned challenges in CC fraud detection by employing a distribution-based one-class function and the walking embedding, without reliance on labeled data or fine-tuning in downstream. Additionally, LLM40FD enhances the model’s ability to detect fraudulent patterns and define robust decision boundaries. This is achieved through a dual-augmentation strategy and implicit contrastive learning, which generate enriched positive and negative samples. Our experiments demonstrate that LLM40FD not only achieves state-of-the-art (SOTA) performance in the full-shot setting but also exhibits strong zero-shot capability even with limited training data. Furthermore, we conduct additional experiments to validate the effectiveness and working mechanism of LLM40FD.

Multiview Subspace Clustering (MvSC) has demonstrated impressive clustering performance on multiview data. Most existing methods rely on either raw features or reduced-redundancy data for subspace representation learning, followed by spectral clustering to derive the final results. However, these methods maintain a fixed feature space during subspace learning, which limits information propagation and compromises both representation quality and clustering performance. To address this issue, this article proposes an adaptive dictionary learning approach for MvSC (AMvSC), which seamlessly integrates redundancy reduction and representation learning within a unified framework to facilitate mutual information propagation. Specifically, an adaptive dictionary learning strategy is designed to automatically reduce redundancy and noise in the original feature space during the subspace representation learning process. This strategy ensures effective information exchange, thereby enhancing the quality of the learned representations. Additionally, low-rank constraints, combined with smoothness and diversity regularization, are applied to further refine the subspace representations and comprehensively capture complex correlations among samples. Finally, an alternating optimization algorithm is developed to iteratively update the unified learning model. Extensive experiments validate the effectiveness and superiority of the proposed method.

Broad learning system (BLS) is renowned for its excellent generalization and high efficiency in data classification. However, when confronted with class imbalance problems, BLS treats all samples as equally important, resulting in performance degradation. In addition, the presence of noise and outliers in imbalanced data further complicates BLS's ability to handle real-world classification problems. To address these challenges, this article proposes a graph-embedding intuitionistic fuzzy adaptive broad learning system (GEIB). The graph embedding strategy proposed by GEIB leverages the geometric topology of the data and class-specific information, effectively capturing variability among imbalanced samples and improving class separability. Furthermore, we introduce intuitionistic fuzzy theory. The BLS integrated with it considers both the homogeneity and heterogeneity of sample neighborhoods, enabling it to address uncertainty and imprecision in the data. It further differentiates clean samples from noisy ones in imbalanced datasets, thereby enhancing model robustness. To further investigate the prior distribution information of imbalanced data, we design an adaptive class-specific penalty mechanism based on global distribution and local density information. This mechanism accounts for both intraclass and interclass density information and class global distribution. We verify the superiority of our method by conducting a comparison with current approaches using real-world datasets that include both Gaussian noise and noise-free versions.

Since cross-modal hashing requires minimal storage and computation, it is becoming increasingly popular with the exponential growth of multimedia content on the internet. However, the lack of accurate supervisory data has curtailed the effectiveness of unsupervised hashing techniques. Conversely, supervised hashing strategies necessitate considerable human and financial resources for data annotation. To address this limitation, we propose a novel semi-supervised cross-modal hashing method called Enhanced Cross-Modal Hashing via Hybrid Distillation and Structural Refinement (HDSR). Specifically, we first learn the features of inter-modal and inter-instance similarity relationships through pointwise semantic alignment and listwise similarity partial order learning, respectively, to extract refined structural representations from partially labeled data. Secondly, by fusing inter-modal similarity to construct higher-order affinity matrices, we precisely delineate the semantic correlation information across cross-modal data, facilitating stable self-supervised training of unlabeled data through the application of momentum fusion strategies. Finally, the refined structural representation of labeled data is transferred into unlabeled branches through hybrid distillation, enhancing the performance of cross-modal hash learning by generating compact and accurate hash codes. The proposed HDSR is compared with several state-of-the-art deep cross-modal hashing methods on three widely used benchmark databases, and the experimental results verify its efficiency and superiority.

In the swiftly advancing realm of information retrieval, unsupervised cross-modal hashing has emerged as a focal point of research, taking advantage of the inherent advantages of the multifaceted and dynamism inherent in multimedia data. Existing unsupervised cross-modal hashing methods rely mainly on initial pre-trained correlations among cross-modal features, and the inaccurate neighborhood correlations impacts the presentation of common semantics throughout the optimization. To address the aforementioned issues, we proposeEnsemblePrototypeNetworks (EPNet), which delineates class attributes of cross-modal instances through an ensemble clustering methodology. EPNet seeks to extract correlation information between instances by leveraging local correlation aggregation and ensemble clustering from multiple perspectives, aiming to reduce initialization effects and enhance cross-modal representations. Specifically, the local correlation aggregation is first proposed within a batch of semantic affinity relationships to generate a precise and compact hash code among cross-modal instances. Secondly, the ensemble prototype module is employed to discern the class attributes of deep features, thereby aiding the model in extracting more universally applicable feature representations. Thirdly, an early attempt to constrict the representational congruity of local semantic affinity relationships and deep feature ensemble prototype correlations using cross-task consistency loss aims to enhance the representation of cross-modal common semantic features. Finally, EPNet outperforms several state-of-the-art cross-modal retrieval methods on three real-world image-text datasets in extensive experiments.

Time-series anomaly detection has gained considerable prominence in numerous practical applications across various domains. Nonetheless, the scarcity of labels leads to the neglect of anomalous patterns in data, as well as the inherent complexities and variances in the definitions of temporal anomalies, pose significant challenges for insufficient recognition of anomaly patterns. In addition, real-time anomaly detection poses high demands on low computational cost and model robustness, presenting substantial obstacles for unsupervised time-series anomaly detection. In this article, we propose the data-driven spontaneous perturbation based on the sequence-image strategy and temporal anomaly knowledge enhancement strategy based on artificial anomalous data pairs to enhance the cognition of abnormal knowledge in unsupervised scenarios. On this basis, we propose the denoising autoencoder based on the broad learning system (DBLS-AE), which sufficiently learns the anomalous patterns, achieving efficient anomaly detection with low computational costs. To enhance the robustness in handling complex and diverse temporal anomalies, we further propose the progressive diversity denoising autoencoders based on the broad learning system (PddBLS-AE), which gradually prioritizes challenging samples and constructs a diverse ensemble of DBLS-AEs, markedly improving both performance and robustness. By innovatively utilizing the broad learning system (BLS), PddBLS-AE achieves accelerated training compared with advanced deep learning models. Comprehensive evaluations across multiple datasets robustly substantiate the efficacy of PddBLS-AE.

Time series anomaly detection is the process of identifying anomalies within time series data. The primary challenge of this task lies in the necessity for the model to comprehend the characteristics of time-independent and abnormal data patterns. In this study, a novel algorithm called adaptive memory broad learning system (AdaMemBLS) is proposed for time series anomaly detection. This algorithm leverages the rapid inference capabilities of the broad learning algorithm and the memory bank's capacity to differentiate between normal and abnormal data. Furthermore, an incremental algorithm based on multiple data augmentation techniques is introduced and applied to multiple ensemble learners, thereby enhancing the model's effectiveness in learning the characteristics of time series data. To bolster the model's anomaly detection capabilities, a more diverse ensemble approach and a discriminative anomaly score are recommended. Extensive experiments conducted on various real-world datasets demonstrate that the proposed method exhibits superior inference speed and more accurate anomaly detection compared to the existing competitors. A detailed experimental investigation is presented to elucidate the effectiveness of the proposed method and the underlying reasons for its efficacy.

Despite the prevalence of reconstruction-based deep learning methods, time-series anomaly detection (TSAD) remains a tremendous challenge. Existing approaches often struggle with limited temporal contexts, insufficient representation of normal patterns, and flawed evaluation metrics, all of which hinder their effectiveness in detecting anomalous behavior. To address these issues, we introduce a simple dissimilarity-based approach for time-series anomaly detection (SimAD). Specifically, SimAD first incorporates a patching-based feature extractor capable of processing extended temporal windows and employs the EmbedPatch encoder to fully integrate normal behavioral patterns. Second, we design an innovative ContrastFusion module in SimAD, which strengthens the robustness of anomaly detection by highlighting the distributional differences between normal and abnormal data. Third, we introduce two robust enhanced evaluation metrics, unbiased affiliation (UAff) and normalized affiliation (NAff), designed to overcome the limitations of existing metrics by providing better distinctiveness and semantic clarity. The reliability of these two metrics has been demonstrated by both theoretical and experimental analyses. Experiments conducted on seven diverse time-series datasets clearly demonstrate SimAD's superior performance compared with state-of-the-art (SOTA) methods, achieving relative improvements of 19.85% on ${F}1$ , 4.44% on Aff-F1, 77.79% on NAff-F1, and 9.69% on AUC on six multivariate datasets. Code and pretrained models are available at https://github.com/EmorZz1G/SimAD.

Clustering methods achieve performance improvement by jointly learning representation and cluster assignment. However, they do not consider the confidence of pseudo-labels which are not optimal as supervised information, resulting into error accumulation. To address this issue, we propose a Robust Pseudo-labeling for Semantic Clustering (RPSC) approach, which includes two stages. In the first stage (RPSC-Self), we design a semantic pseudo-labeling scheme by using the consistency of samples, i.e., samples with same semantics should be close to each other in the embedding space. To exploit robust semantic pseudo-labels for self-supervised learning, we propose a soft contrastive loss (SCL) which encourage the model to believe high-confidence sematic pseudo-labels and be less driven by low-confidence pseudo-labels. In the second stage (RPSC-Semi), we first determine the semantic pseudo-label of a sample based on the distance between itself and cluster centers, followed by screening out reliable semantic pseudo-label by exploiting the consistency. These reliable pseudo-labels are used as supervised information in the pseudo-semi-supervised learning algorithm to further improve the performance. Experimental results show that RPSC outperforms 18 competitive clustering algorithms significantly on six challenging image benchmarks. In particular, RPSC achieves an accuracy of 0.688 on ImageNet-Dogs, which is an up to 24% improvement, compared with the second-best method. Meanwhile, we conduct ablation studies to investigate effects of different augmented strategies on RPSC as well as contributions of terms in SCL to clustering performance. Besides, experimental results indicate that SCL can be easily integrated into existing clustering methods and bring performance improvement.

The application of artificial intelligence technology has greatly enhanced and fortified the safety of energy pipelines, particularly in safeguarding against external threats. The predominant methods involve the integration of intelligent sensors to detect external vibration, enabling the identification of event types and locations, thereby replacing manual detection methods. However, practical implementation has exposed a limitation in current methods - their constrained ability to accurately discern the spatial dimensions of external signals, which complicates the authentication of threat events. Our research endeavors to overcome the above issues by harnessing deep learning techniques to achieve a more fine-grained recognition and localization process. This refinement is crucial in effectively identifying genuine threats to pipelines, thus enhancing the safety of energy transportation. This paper proposes a radial threat estimation method for energy pipelines based on distributed optical fiber sensing technology. Specifically, we introduce a continuous multi-view and multi-domain feature fusion methodology to extract comprehensive signal features and construct a threat estimation and recognition network. The utilization of collected acoustic signal data is optimized, and the underlying principle is elucidated. Moreover, we incorporate the concept of transfer learning through a pre-trained model, enhancing both recognition accuracy and training efficiency. Empirical evidence gathered from real-world scenarios underscores the efficacy of our method, notably in its substantial reduction of false alarms and remarkable gains in recognition accuracy. More generally, our method exhibits versatility and can be extrapolated to a broader spectrum of recognition tasks and scenarios.

Efficiently capturing the complex spatiotemporal representations from large-scale traffic data with uneven data quality remains to be a challenging task. In considering of the dilemma, this work employs the advanced contrastive learning and proposes a novel Spatial-Temporal Synchronous Contextual Contrastive Learning (STS-CCL) model. First, we elaborate the basic and strong augmentation methods for spatiotemporal graph data. Second, we introduce a Spatial-Temporal Synchronous Contrastive Module (STS-CM) to simultaneously capture the decent spatial-temporal dependencies and realize graph-level contrasting. To further discriminate node individuals in negative filtering, a Semantic Contextual Contrastive method is designed based on semantic features and spatial heterogeneity, achieving node-level contrastive learning along with negative filtering. Finally, we present a hard mutual-view contrastive training scheme and extend the classic contrastive loss to an integrated objective function, yielding better performance. Extensive experiments and evaluations demonstrate that building a predictor upon STS-CCL contrastive learning model gains superior performance than existing traffic forecasting benchmarks. The proposed STS-CCL is highly suitable for large datasets with only a few labeled data and other spatiotemporal tasks with data scarcity issue.

In the current era of big data, securing computer networks and preventing cyberattacks have become a major challenge. In this regard, we present a novel approach for addressing the imbalanced problem of network traffic data. Specifically, the proposed method MC-OCBLS, leverages the maximum correntropy criterion (MCC) to develop a one-class broad learning system. The robustness of the one-class classification model is enhanced by maximizing the correlation entropy. Moreover, considering the limited generalization capability of individual models, we propose the boosting ensemble model SMC-OCBLS, which assigns higher weights to misclassified samples and classifiers with lower error rates. To increase the variety of classifiers in the ensemble framework, feature columns are randomly disrupted during training to construct different feature spaces. Training multiple hybrid classifiers using various feature spaces further enhances the accuracy and generalizability of the model. The experimental results demonstrate the superiority of the proposed model over other advanced approaches.

Accurate and timely natural gas consumption forecasts are essential for energy policy formulation, natural gas scheduling, and pipeline network design. However, it remains a significant challenge because natural gas consumption is highly nonlinear and irregular with complex cycles. In this article, we propose a new spatial-temporal multiscale transformer network framework that exploits dynamic spatial dependence among users and temporal support of historical multivariate data to improve the accuracy of short-term natural gas consumption forecasting. A novel graph neural network model is developed to capture the spatial dependencies relationships among users by considering the fixed and dynamic connectivity. Compared with other approaches, we validate the effectiveness of the proposed model and its ability to capture fine-grained and spatial-temporal dependencies on a real dataset.

Feature selection is a highly regarded research area in the field of data mining, as it significantly enhances the efficiency and performance of high-dimensional data analysis by eliminating redundant and irrelevant features. Despite the ease of data acquisition, labeling data remains a laborious and expensive task. To leverage the abundance of unlabeled data, researchers have proposed various feature selection methods that operate with limited labels, including semi-supervised feature selection and unsupervised feature selection. However, a comprehensive review encompassing a thorough overview of feature selection algorithms with limited labels is lacking. To bridge this gap, this paper conducts a comprehensive exploration of feature selection methods specifically tailored to limited-label scenarios. These methods are systematically classified into two primary categories: semi-supervised and unsupervised feature selection. Additionally, by introducing a novel taxonomy and discussing future challenges, this survey aims to provide researchers with a comprehensive and in-depth understanding of feature selection in limited-label scenarios. Moreover, it aims to offer valuable insights that can guide further research and development in this domain.

Imbalanced data poses a substantial challenge to conventional classification methods, which often disproportionately favor samples from the majority class. To mitigate this issue, various oversampling techniques have been deployed, but opportunities for optimizing data distributions remain underexplored. By exploiting the ability of metric learning to refine the sample distribution, we propose a novel approach, Imbalance Large Margin Nearest Neighbor (ILMNN). Initially, ILMNN is applied to establish a latent feature space, pulling intra-class samples closer and distancing inter-class samples, thereby amplifying the efficacy of oversampling techniques. Subsequently, we allocate varying weights to samples contingent upon their local distribution and relative class frequency, thereby equalizing contributions from minority and majority class samples. Lastly, we employ Kullback-Leibler (KL) divergence as a safeguard to maintain distributional similarity to the original dataset, mitigating severe intra-class imbalances. Comparative experiments on various class-imbalanced datasets verify that our ILMNN approach yields superior results.

Broad learning system (BLS) is a simple yet efficient learning algorithm that only needs to train a three-layer feedforward neural network. Although various BLS variants have been designed for supervised learning, none have been used for unsupervised learning. This paper proposes BLS-AE, a novel data clustering scheme that seamlessly combines BLS and auto-encoder. Then, graph regularization is introduced into BLS-AE to increase the capability of learning intrinsic structures in data and adaptation to various data simultaneously, which is termed BLSg-AE. Moreover, different concatenation styles of feature and enhancement nodes are investigated for reusing the learned features, followed by designing two special strategies (i.e., pruning optimization and incremental learning) to reduce the parameter scale significantly and improve performance, which is termed xBLSg-AE. To address the performance instability issue caused by random subspace in a single xBLSg-AE, the x-cascade broad learning system graph regularization multi-auto-encoder (xBLSg-MAE) algorithm is proposed. Extensive experiments are conducted on multiple real data sets to demonstrate that the proposed methods are more effective and robust than competing approaches.

Due to the advantages of fast training speed and competitive performance, Broad Learning System (BLS) has been widely used for classification tasks across various domains. However, the random weight generation mechanism in BLS makes the model unstable, and the performance of BLS may be limited when dealing with some complex datasets. On the other hand, the instability of BLS brings diversity to ensemble learning, and ensemble methods can also reduce the variance and bias of the single BLS. Therefore, we propose an ensemble learning algorithm based on BLS, which includes three modules. To improve the stability and generalization ability of BLS, we utilize BLS as the base classifier in an AdaBoost framework first. Taking advantage of the incremental learning mechanism of BLS, we then propose a selective ensemble method to raise the accuracy and diversity of the BLS ensemble method. In addition, based on the former selective Adaboost framework, we suggest a hierarchical ensemble algorithm, which combines sample and feature dimensions to further improve the fitting ability of the ensemble BLS. Extensive experiments have demonstrated that the proposed method performs better than the original BLS and other state-of-the-art models, proving the effectiveness and versatility of our proposed approaches.

Consensus clustering methods provide better performance by fusing multiple clustering solutions in terms of accuracy, robustness and stability. However, most current methods suffer from different challenges: i) the high-dimensional problem; ii) limitations of single clustering method; iii) the optimal number of clusters selecting for a certain validity measure; iv) redundant clustering candidate attributes. To overcome the above limitations, we propose a hybrid clustering solutions fusion method based on gated three-way decision (HCFG) for data analysis. By integrating multiple clustering solutions and executing information fusion, HCFG enjoys four properties: (1) multiple random subspace generation strategy is utilized to generate diverse low-dimensional subspaces effectively; (2) a fusion framework that considers characteristics of both the soft clustering and hard clustering methods is designed, in which potential boundary of feature attribute sets is explored; (3) the optimal number of clusters is set by utilizing multiple clustering validity indices; (4) clustering solutions is considered as attributes and a gated three-way decision method is proposed to adaptively conduct attribute reductions. Extensive comparative experiments on 24 real-world data sets demonstrates the effectiveness and superiority of HCFG. Moreover, nonparametric tests are conducted to compare HCFG with multiple consensus clustering methods.

With the vigorous development of Industry 4.0, industrial Big Data has turned into the core element of the Industrial Internet of Things. As one of the most fundamental and indispensable components in industrial cyber-physical systems (CPS), intelligent anomaly detection is still an essential and challenging issue. However, with the development of the network, there may exist unknown types of attacks, which are difficult to collect. Facing one-class industrial intrusion detection scenario that the collected training data only includes normal state, the one-class broad learning system (OCBLS) and the stacked OCBLS (ST-OCBLS) algorithms are developed. Benefiting from the characteristics of BLS, our proposed approaches retain the advantage of efficient training process. Moreover, the high-level hidden features of the network traffic data can be learned through the progressive encoding and decoding mechanism in ST-OCBLS. Extensive comparative experiments on several real-world intrusion detection tasks are carried out to demonstrate that our proposed methods have competitive performance and high efficiency in the face of complex network data and diversified types of intrusions. Overall, this article provides a new alternative solution for network intrusion detection in Industry 4.0.

Ensemble clustering has an advantage in producing a more promising and robust clustering result by combining multiple partitions strategically. The quality of both base partitions and co-association matrix plays an essential role in improving the consensus partition. However, the current ensemble clustering methods have several limitations: 1) The noise in high-dimensional feature space is ignored; 2) The independent base partition generation process does not pay attention to ambiguous samples; 3) The co-association matrix and the weights of base partitions commonly lack of theoretical optimization. In order to address these issues, we propose an adaptive ensemble clustering framework with boosting BLS-based autoencoder (BoostAEC). In the generation step, a boosting BLS-based autoencoder (BoostBLSAE) is designed to generate base partitions sequentially, which learns compressed feature subspaces for ambiguous samples and adaptively evaluates the corresponding weights of reliability. In the integration step, we construct a fuzzy membership function to capture the inter-cluster correlation and explicitly propose a consensus objective function to optimize the unified co-association matrix by considering the weighted base partitions. Extensive experiments on various real-world datasets demonstrate the superior performance of BoostAEC to the state-of-the-art ensemble clustering methods.

With effective performance and fast training speed, broad learning system (BLS) has been widely developed in recent years, which provides a new way for network training. However, the randomly generated feature nodes and enhancement nodes in the BLS network may have redundant and inefficient features, which will affect the subsequent classification performance. In response to the above issues, we propose a series of self-encoding networks based on BLS from the perspective of unsupervised feature extraction. These include the single hidden layer autoencoder built on the basis of BLS(BLS-AE), the stacked BLS-based autoencoder (ST-BLS), the sparse BLS-based autoencoder (SP-BLS), and the stacked sparse BLS-based autoencoder(SS-BLS). The proposed BLS-based self-encoding networks retain the advantage of efficient BLS model training, and overcome the time-consuming defect of iterative parameter optimization in traditional self-encoding networks. In addition, the higher-level abstract features of the input data can be learned through the progressive encoding and decoding process. Combining$L_1$regularization to train the parameters can further enhance the robustness of the extracted features. Extensive comparative experiments on real-world data sets demonstrate the superiority of the proposed methods in terms of both effectiveness and efficiency.

Accurate citywide traffic flow prediction is of great importance to intelligent transportation system. Existing methods typically assume the complete citywide traffic data can be obtained in real-time, which is impossible in applications. Furthermore, many recent works only consider one single kind of spatial correlation in traffic network when building graph representations. This work proposes an adversarial learning framework named Multi-Graph Convolutional Generative Adversarial Networks (MGC-GAN) to address the aforementioned challenges. To generate citywide traffic flow predictions using limited traffic data, we construct three kinds of graphs using easily accessed geographical and semantic information to model the complex spatial correlations in citywide transportation networks. Following that, a parallel GCN layer is designed to separately process multiple graphs. In addition, we design the Parallel Graph Convolution and Temporal Convolution Module (PGTCM) to effectively capture the heterogeneous spatial-temporal dependencies. Extensive experiments are carried out on two citywide traffic datasets, demonstrating that MGC-GAN outperforms several state-of-the-art baseline methods.

Broad learning system (BLS) is a novel and efficient model, which facilitates representation learning and classification by concatenating feature nodes and enhancement nodes. In spite of the efficient properties, BLS is still suboptimal when facing with imbalance problem. Besides, outliers and noises in imbalanced data remain a challenge for BLS. To address the above issues, in this paper we first propose a weighted BLS, which assigns a weight to each training sample, and adopt a general weighting scheme, which augments the weight of samples from the minority class. To further explore the prior distribution of original data, we design a density based weight generation mechanism to guide the specific weight matrix generation and propose the adaptive weighted broad learning system (AWBLS). This mechanism considers the inter-class and intra-class distance simultaneously in the density calculation. Finally, we propose the incremental weighted ensemble broad learning system (IWEB) by utilizing a progressive mechanism to further improve the stability and robustness of AWBLS. Extensive comparative experiments on 38 real-world data sets verfy that IWEB outperforms most of the imbalance ensemble classification methods.

Graph-based methods have achieved impressive performance on semisupervised classification (SSC). Traditional graph-based methods have two main drawbacks. First, the graph is predefined before training a classifier, which does not leverage the interactions between the classifier training and similarity matrix learning. Second, when handling high-dimensional data with noisy or redundant features, the graph constructed in the original input space is actually unsuitable and may lead to poor performance. In this article, we propose an SSC method with novel graph construction (SSC-NGC), in which the similarity matrix is optimized in both label space and an additional subspace to get a better and more robust result than in original data space. Furthermore, to obtain a high-quality subspace, we learn the projection matrix of the additional subspace by preserving the local and global structure of the data. Finally, we intergrade the classifier training, the graph construction, and the subspace learning into a unified framework. With this framework, the classifier parameters, similarity matrix, and projection matrix of subspace are adaptively learned in an iterative scheme to obtain an optimal joint result. We conduct extensive comparative experiments against state-of-the-art methods over multiple real-world data sets. Experimental results demonstrate the superiority of the proposed method over other state-of-the-art algorithms.

The class imbalance problem has posed a leading challenge in real-world applications. Traditional methods focus on either the data level or algorithm level to solve the binary classification problem on imbalanced data, and seldom consider searching an effective transformation for classification. Besides, the undersampling process adopted in them is always subjective and unilateral. To address the above issues, we first propose a hybrid classifier ensemble (HCE) framework to conduct binary imbalanced data classification, which mainly includes a metric-based data space transformation (MDST) and an adaptive two-stage undersampling process (ATUP). The MDST aims to find a more appropriate embedding space for original imbalance data sets, and the ATUP considers both informative and representative samples to generate balanced data sets. Furthermore, we design a progressive HCE (PHCE) framework to improve the performance of HCE by utilizing a progressive mechanism with local and global evaluation criteria to select ensemble members. Extensive comparative experiments conducted on 28 real-world data sets exhibit that our method PHCE outperforms the majority of imbalance ensemble classification approaches.

Learning and classification of imbalanced data is a quite common challenge in machine learning. In order to generate more high-quality minority samples in imbalanced data and help solve the classification process, this paper proposes a novel local tangent generative adversarial network (LT-GAN). In LT-GAN, a local tangent based generator is designed to generate realistic and diverse minority class samples by learning the local tangent space of original minority samples. Meanwhile, a two-function discriminator is explored to judge the authenticity of samples and distinguish the majority samples from the minority samples. With the synthesis of minority class samples, the generator and discriminator are trained together by using adversarial learning. Experiments and comparisons show that our proposed LT-GAN outperforms other techniques and significantly improves the classification performance of imbalanced data.

The newly proposed Broad Learning System (BLS) offers an alternative way to deep learning which saves a time-consuming training process and powerful computing resources. However, the randomly generated feature nodes and lots of enhancement nodes in BLS may reduce the performance of the final classifier. Aiming at the problems in randomly generated feature nodes which suffer from unpredictability and need guidance, this paper proposes an adaptive feature nodes evolutionary algorithm (AFNE) to extract better features; While broad learning neural network often requires a large number of enhancement nodes parameters to ensure its performance, which easily leads to the redundancy or dependency between features, as well as the performance degradation of the final model. Therefore, this article also proposes a new criterion based on node sensitivity to prune the network enhancement layer nodes to remove redundant nodes, reduce the network scale, and increase the generalization ability. The proposed algorithm ADP-BLS can improve the accuracy and generalization performance of the final classifier through the evolution of feature nodes and the pruning of enhancement nodes. Extensive comparative experiments on real world data sets verify the effectiveness of the proposed ADP-BLS. At the same time, when verifying the module validity of the innovative algorithms added in this article, experiments also show that the AFNE and pruning method integrated into BLS can improve the model to a certain extent.

The class imbalance problem has become a leading challenge. Although conventional imbalance learning methods are proposed to tackle this problem, they have some limitations: 1) undersampling methods suffer from losing important information and 2) cost-sensitive methods are sensitive to outliers and noise. To address these issues, we propose a hybrid optimal ensemble classifier framework that combines density-based undersampling and cost-effective methods through exploring state-of-the-art solutions using multi-objective optimization algorithm. Specifically, we first develop a density-based undersampling method to select informative samples from the original training data with probability-based data transformation, which enables to obtain multiple subsets following a balanced distribution across classes. Second, we exploit the cost-sensitive classification method to address the incompleteness of information problem via modifying weights of misclassified minority samples rather than the majority ones. Finally, we introduce a multi-objective optimization procedure and utilize connections between samples to self-modify the classification result using an ensemble classifier framework. Extensive comparative experiments conducted on real-world data sets demonstrate that our method outperforms the majority of imbalance and ensemble classification approaches.