Multi-view unsupervised feature selection (MV-UFS) has recently gained significant attention as an effective technique for handling high-dimensional multi-view data. Despite recent progress, three flaws are present in most existing MV-UFS methods. First, they predominantly pay attention to the feature dimensionality while neglecting sample size considerations, typically resulting in cubic time complexity concerning sample size (i.e.$\mathcal {O}(n^{3})$) that hinders them in real-world applications. Second, existing methods primarily focus on the relationships between pairwise views, failing to leverage the complex interactions among multiple views effectively. Third, they generally overlook crucial graph semantic information during graph fusion. An ingenious MV-UFS method is presented in this paper, namedJoint Information Interaction and Semantic Fusion for Multi-View Unsupervised Feature Selection (JIISF), to overcome these limitations. Specifically, JIISF devises a multi-view double reduction learning framework that projects original data into a low-dimensional embedding space and dynamically picks out representative anchors, eliminating redundant information while constructing compact view-specific anchor graphs. Meanwhile, JIISF presents a cross-view information interaction scheme, which enables comprehensive information interaction among multiple views rather than being limited to pairwise views. A semantic alignment fusion strategy is presented to learn a diverse, consistent anchor graph with an explicit semantic alignment module, which can preserve diversity while maintaining consistency. Finally, an alternating optimization algorithm is devised to resolve the proposed JIISF, and experimental results on real-life datasets validate its advantage compared with some state-of-the-art UFS and MV-UFS methods.

Graph-based feature selection methods can capture the most discriminative subset of features in multi-label data and have made great strides in recent years. However, most existing methods have three drawbacks: (1) Only binary pairwise relationship is difficult to capture latent structural information in high-dimensional data fully. (2) Using only one distance metric cannot accurately mine the similarity relationship between instances. (3) Ignore the problem that some of the non-obvious labels are missing when labels are abundant. Therefore, this paper proposes a graph diffusion with dual-distance metrics for missing multi-label feature selection named GDMMFS. GDMMFS uses both Euclidean and cosine distances to explore instance correlations and then uses diffusion techniques to transform binary relationship into quaternary relationship, thus obtaining a stable similarity graph. Moreover, GDMMFS utilizes adaptive graphs to learn latent structure information to improve the model’s immunity to interference. Finally, the ℓ 2 , 1 -norm is used as a sparse constraint to guide the weight matrix to capture the most discriminative subset of features. To further consider the possibility of missing label data, GDMMFS constructed a logic matrix to guide the numerical labels in recovering the missing information. Comparison experiments with state-of-the-art related algorithms demonstrate the superiority of the proposed algorithm, and ablation experiments demonstrate the effectiveness of graph diffusion with two-distance metrics.

Air quality modeling and forecasting has become a key problem in environmental protection. The existing prediction models typically require large‐scale and high‐quality historical data to achieve better performance. However, insufficient data volume and significant differences between data distribution across different regions will definitely reduce the effectiveness of the model reuse. To address the above issues, we propose a novel hybrid recurrent network based on domain adversarial transfer to achieve a stronger generalization ability when training air quality data from multisource domains. The proposed model mainly consists of three fundamental modules, i.e., feature extractor, regression predictor, and domain classifier. One‐dimensional convolutional neural networks (1D‐CNNs) are used to extract temporal feature of data from source and target stations. Bi‐directional gated recurrent unit (bi‐GRU) and bi‐directional long short‐term memory (bi‐LSTM) are utilized to learn temporal dependencies pattern of multivariate time series data. Two adversarial transfer strategies are employed to ensure that our model is capable of finding domain invariant representations automatically. Experiments with different number of source domains are conducted to demonstrate the effectiveness of the proposed domain transfer strategies. The experimental results also show that our composite model has superior performance for forecasting air quality in various regions. As further evidence, the adversarial training method could promote the positive transfer and alleviate the negative effect of irrelevant source data. Besides, our model exhibits preferable generalization capability as more robust prediction results are achieved on both unseen target domains and original source domains.

Traditional multilabel feature selection (MFS) typically relies on pre-computing global information within the feature space. However, in real-world applications, features are dynamically generated and continuously arrive over time, known as streaming features, rendering many existing approaches ineffective. Some MFS methods for streaming features have been developed, several challenges persist: (1) Previous research often uses certain strategies to model streaming feature evaluation, failing to process fuzzy information effectively; (2) The maximum correlation between features and class is emphasized, while inter-class separability is ignored, leading to inaccurate feature evaluation; (3) The continuous influx of streaming features brings the dynamics and unknowns to data distribution, has been largely overlooked in previous work; (4) Streaming feature selection requires immediate feedback on newly arriving features, posing challenges to the algorithm's real-time responsiveness. Motivated by these observations, this paper introduces a novel online MFS strategy for streaming features. First, the weighted manifold distance is designed, and the fuzzy manifold similarity learning strategy is formalized to analyze the instance relationships of unknown distribution. Second, the fuzzy manifold intra-class correlation and inter-class separability are devised to quantify feature discriminability. Finally, a novel multilabel streaming feature analysis framework is established, with feature discriminability as the guiding factor. Incoming features are categorized as weakly relevant, strongly relevant, or redundant, culminating in generating a reliable feature selection subset. Extensive experiments on fifteen public datasets demonstrate that our algorithm achieves competitive performance compared to nine state-of-the-art offline and online algorithms.

Feature selection aims to remove irrelevant or redundant features and thereby remain relevant or informative features so that it is often preferred for alleviating the dimensionality curse, enhancing learning performance, providing better readability and interpretability, and so on. Data that contain numerical and categorical representations are called heterogeneous data, and they exist widely in many real-world applications. Neighborhood rough set (NRS) can effectively deal with heterogeneous data by using neighborhood binary relation, which has been successfully applied to heterogeneous feature selection. In this article, the NRS model as a unified framework is used to design a feature selection method to handle categorical, numerical, and heterogeneous data. First, the concept of neighborhood combination entropy (NCE) is presented. It can reflect the probability of pairs of the neighborhood granules that are probably distinguishable from each other. Then, the conditional neighborhood combination entropy (cNCE) based on NCE is proposed under the condition of considering decision attributes. Moreover, some properties and relationships between cNCE and NCE are derived. Finally, the functions of inner and outer significances are constructed to design a feature selection algorithm based on cNCE (FScNCE). The experimental results show the effectiveness and superiority of the proposed algorithm.

Fuzzy rough set theory can model uncertainty in data and has been applied to feature selection for machine learning tasks. The existence of noise in data is one of the reasons for data uncertainty. However, most classical fuzzy rough set models are often sensitive to the noise in data, which somewhat degrades their applicability to process uncertainty of data. Furthermore, a robust feature evaluation function is nontrivial in a fuzzy rough set model as a nonoptimal feature subsets may be selected due to the perturbations from redundant features. In this article, we delve into local density and indispensable features for fuzzy rough feature selection to address these challenges. We first propose a local density-based fuzzy rough set (LDFRS) model to tackle noisy data. Mutual information is then plugged into the proposed LDFRS model to evaluate uncertainty in data. A joint feature evaluation function on the indispensability and relevance of features is constructed to evaluate the significance of features. On this basis, a fuzzy rough feature selection algorithm is built upon the LDFRS model. Experimental results using four typical classifiers demonstrate the robustness and effectiveness of the proposed model including our feature selection algorithm and its superiority against baseline methods.

Feature selection refers to choose an optimal non-redundant feature subset with minimal degradation of learning performance and maximal avoidance of data overfitting. The appearance of large data explosion leads to the sequential execution of algorithms are extremely time-consuming, which necessitates the scalable parallelization of algorithms by efficiently exploiting the distributed computational capabilities. In this paper, we present parallel feature selection algorithms underpinned by a rough hypercuboid approach in order to scale for the growing data volumes. Metrics in terms of rough hypercuboid are highly suitable to parallel distributed processing, and fits well with the Apache Spark cluster computing paradigm. Two data parallelism strategies, namely, vertical partitioning and horizontal partitioning, are implemented respectively to decompose the data into concurrent iterative computing streams. Experimental results on representative datasets show that our algorithms significantly faster than its original sequential counterpart while guaranteeing the quality of the results. Furthermore, the proposed algorithms are perfectly capable of exploiting the distributed-memory clusters to accomplish the computation task that fails on a single node due to the memory constraints. Parallel scalability and extensibility analysis have confirmed that our parallelization extends well to process massive amount of data and can scales well with the increase of computational nodes.

The selection of prominent features for building more compact and efficient models is an important data preprocessing task in the field of data mining. The rough hypercuboid approach is an emerging technique that can be applied to eliminate irrelevant and redundant features, especially for the inexactness problem in approximate numerical classification. By integrating the meta-heuristic-based evolutionary search technique, a novel global search method for numerical feature selection is proposed in this article based on the hybridization of the rough hypercuboid approach and binary particle swarm optimization (BPSO) algorithm, namely RH-BPSO. To further alleviate the issue of high computational cost when processing large-scale datasets, parallelization approaches for calculating the hybrid feature evaluation criteria are presented by decomposing and recombining hypercuboid equivalence partition matrix via horizontal data partitioning. A distributed meta-heuristic optimized rough hypercuboid feature selection (DiRH-BPSO) algorithm is thus developed and embedded in the Apache Spark cloud computing model. Extensive experimental results indicate that RH-BPSO is promising and can significantly outperform the other representative feature selection algorithms in terms of classification accuracy, the cardinality of the selected feature subset, and execution efficiency. Moreover, experiments on distributed-memory multicore clusters show that DiRH-BPSO is significantly faster than its sequential counterpart and is perfectly capable of completing large-scale feature selection tasks that fail on a single node due to memory constraints. Parallel scalability and extensibility analysis also demonstrate that DiRH-BPSO could scale out and extend well with the growth of computational nodes and the volume of data.

Feature selection is an important topic in data mining and machine learning, which aims to select an optimal feature subset for building effective and explainable prediction models. This article introduces Rough Hypercuboid based Distributed Online Feature Selection (RHDOFS) method to tackle two critical challenges of Volume and Velocity associated with Big Data. By exploring the class separability in the boundary region of rough hypercuboid approach, a novel integrated feature evaluation criterion is proposed by examining not only the explicit patterns contained in the positive region but also the useful implicit patterns derived from the boundary region. An efficient online feature selection method for streaming feature scenario is developed to identify relevant and nonredundant features in an incremental iterative fashion. Furthermore, a parallel optimization mechanism by combining both data and computational independence is further employed to accelerate the original sequential implementation. An efficient distributed online feature selection algorithm is presented and implemented on the Apache Spark platform to scale for massive amount of data by exploiting the computational capabilities of multicore clusters. Encouraging results of extensive experiments indicate the superiority and notable advantages of the proposed algorithm over the relevant and representative online feature selection algorithms. Empirical tests on scalability and extensibility also demonstrate our distributed implementation significantly reduces the computational times requirements while maintaining the prediction accuracy, and is capable of scaling well in volume of data and number of computing nodes.

Information fusion is capable of fusing and transforming multiple data derived from different sources to provide a unified representation for centralized knowledge mining that facilitates effective decision-making, classification and prediction, etc. Multisource interval-valued data, characterizing the uncertainty phenomena in the data in the form of intervals in different sources, are the most common symbolic data which widely exist in many real-world applications. This paper concentrates on efficient fusing of multisource interval-valued data with the dynamic updating of data sources, involving the addition of new sources and deletion of obsolete sources. We propose a novel data fusion method based on fuzzy information granulation, which translates multisource interval-valued data into trapezoidal fuzzy granules. Given this effectively fusing capability, we develop incremental mechanisms and algorithms for fusing multisource interval-valued data with a dynamic variation of data sources. Finally, extensive experiments are carried out to verify the effectiveness of the proposed algorithms when comparing to six different fusion algorithms. Experimental results show that the proposed fusion method outperforms other related approaches. Furthermore, the proposed incremental fusion algorithms can reduce the computing overhead in comparison with the static fusion algorithm when adding and deleting multiple data sources.

Incremental technique is an efficient mechanism for dealing with dynamic knowledge discovery. The fuzzy probabilistic rough set model on dual universes (FPRSMDU) is an integrated generalization of classic rough set theory (RST) on fuzziness, probability and dual universes. Although a significant number of RST based research efforts have been directed toward developing incremental algorithms to speed up computation of approximations, feature selection, as well as rule extraction in the context of dynamical information systems, there remains lack of effort towards incorporating the incremental method into knowledge updating in the framework of FPRSMDU. Approximations of FPRSMDU are fundamental concepts, which can be used for knowledge discovery in big data or other related work, need to be updated effectively when the objects of two universes vary with time. In light of these issues, an incremental approach for updating approximations in FPRSMDU is proposed while multiple objects inserting into or deleting from the two universes. The validity of the proposed method has been exemplified by employing an illustrative example.

Uncertainty and fuzziness generally exist in real-life data. Approximations are employed to describe the uncertain information approximately in rough set theory. Certain and uncertain rules are induced directly from different regions partitioned by approximations. Approximation can further be applied to datamining-related task, e.g., attribute reduction. Nowadays, different types of data collected from different applications evolve with time, especially new attributes may appear while new objects are added. This paper presents an approach for dynamic maintenance of approximations w.r.t. objects and attributes added simultaneously under the framework of decision-theoretic rough set (DTRS). Equivalence feature vector and matrix are defined first to update approximations of DTRS in different levels of granularity. Then, the information system is decomposed into subspaces, and the equivalence feature matrix is updated in different subspaces incrementally. Finally, the approximations of DTRS are renewed during the process of updating the equivalence feature matrix. Extensive experimental results verify the effectiveness of the proposed methods.

Rule induction method based on rough set theory (RST) has received much attention recently since it may generate a minimal set of rules from the decision system for real-life applications by using of attribute reduction and approximations. The decision system may vary with time, e.g., the variation of objects, attributes and attribute values. The reduction and approximations of the decision system may alter on Attribute Values' Coarsening and Refining (AVCR), a kind of variation of attribute values, which results in the alteration of decision rules simultaneously. This paper aims for dynamic maintenance of decision rules w.r.t. AVCR. The definition of minimal discernibility attribute set is proposed firstly, which aims to improve the efficiency of attribute reduction in RST. Then, principles of updating decision rules in case of AVCR are discussed. Furthermore, the rough set-based methods for updating decision rules in the inconsistent decision system are proposed. The complexity analysis and extensive experiments on UCI data sets have verified the effectiveness and efficiency of the proposed methods.

Incremental learning is an efficient technique for knowledge discovery in a dynamic database, which enables acquiring additional knowledge from new data without forgetting prior knowledge. Rough set theory has been successfully used in information systems for classification analysis. Set-valued information systems are generalized models of single-valued information systems, which can be classified into two categories: disjunctive and conjunctive. Approximations are fundamental concepts of rough set theory, which need to be updated incrementally while the object set varies over time in the set-valued information systems. In this paper, we analyze the updating mechanisms for computing approximations with the variation of the object set. Two incremental algorithms for updating the approximations in disjunctive/conjunctive set-valued information systems are proposed, respectively. Furthermore, extensive experiments are carried out on several data sets to verify the performance of the proposed algorithms. The results indicate the incremental approaches significantly outperform non-incremental approaches with a dramatic reduction in the computational speed.