Aiming at identifying unexpected inputs from unknown classes, out-of-distribution (OOD) detection has emerged as a pivotal approach to enhancing the reliability of machine learning models. This paper focuses on the burgeoning paradigm of post-hoc OOD detection with pre-trained vision-language models (VLMs), where a popular pipeline is to detect OOD inputs by examining their affinities between ID labels and negative labels, i.e., those semantically different from ID labels. Due to the unavailability of target OOD labels, existing works predominantly rely on heuristic rules to mine negative labels from unlabeled wild corpus data. Despite the empirical success, we argue that the power of VLM-based OOD detection has yet to be fully unleashed since the notorious false negative problem is far from addressed in the literature. With this motivation, we are interested in addressing the challenge of mining true negative labels for OOD scoring. To this end, we develop a theoretical framework for correcting the sampling bias of negatives labels by indirectly approximating the distribution of negative labels. Perhaps surprisingly, we show that the debiased negative mining can be naturally converted into Monte-Carlo sampling based on ID labels and the unlabeled wild corpus data. Extensive experiments empirically manifest that our method establishes a new state-of-the-art in a variety of OOD detection setups.

Academic question answering requires reasoning over heterogeneous scholarly graphs, where queries range from simple attribute lookups to multi-hop inference across author--paper--venue structures. Existing retrieval-augmented generation (RAG) systems struggle in this setting due to three limitations: (1) fixed retrieval strategies that do not adapt to varying query complexity, (2) the absence of sufficiency evaluation leading to incomplete or misaligned evidence, and (3) a lack of structured verification against graph facts. To address these issues, we propose an agentic heterogeneous graph RAG method that transforms the three core stages of the RAG pipeline into explicit agentic decision steps. A query-aware retrieval agent analyzes query type and selects an appropriate graph traversal strategy; a sufficiency-aware reranking agent assesses evidence completeness and adaptively expands the retrieved subgraph; and a graph-grounded verification agent checks entity, relation, and attribute correctness before finalizing the answer. Experiments on heterogeneous graphs constructed from OpenAlex and DBLP suggest that our method consistently outperforms strong LLM, graph-augmented RAG, and agent-based baselines.

As a key to accessing research impact, citation dynamics underpins research evaluation, scholarly recommendation, and the study of knowledge diffusion. Citation prediction is particularly critical for newborn papers, where early assessment must be performed without citation signals and under highly long-tailed distributions. We identify two key research gaps: (i) insufficient modeling of implicit factors of scientific impact, leading to reliance on coarse proxies; and (ii) a lack of bias-aware learning that can deliver stable predictions on lowly cited papers. We address these gaps by proposing a Bias-Aware Citation Prediction Framework, which combines multi-agent feature extraction with robust graph representation learning. First, a multi-agent x graph co-learning module derives fine-grained, interpretable signals, such as reproducibility, collaboration network, and text quality, from metadata and external resources, and fuses them with heterogeneous-network embeddings to provide rich supervision even in the absence of early citation signals. Second, we incorporate a set of robust mechanisms: a two-stage forward process that routes explicit factors through an intermediate exposure estimate, GroupDRO to optimize worst-case group risk across environments, and a regularization head that performs what-if analyses on controllable factors under monotonicity and smoothness constraints. Comprehensive experiments on two real-world datasets demonstrate the effectiveness of our proposed model. Specifically, our model achieves around a 13% reduction in error metrics (MALE and RMSLE) and a notable 5.5% improvement in the ranking metric (NDCG) over the baseline methods.

Real-world data streams exhibit inherent non-stationarity characterized by concept drift, posing significant challenges for adaptive learning systems. While existing methods address isolated distribution shifts, they overlook the critical co-evolution of label spaces and distributions under limited supervision and persistent uncertainty. To address this, we formalize Generalized Incremental Learning under Concept Drift (GILCD), characterizing the joint evolution of distributions and label spaces in open-environment streaming contexts, and propose a novel framework called Calibrated Source-Free Adaptation (CSFA). First, CSFA introduces a training-free prototype calibration mechanism that dynamically fuses emerging prototypes with base representations, enabling stable new-class identification without optimization overhead. Second, we design a novel source-free adaptation algorithm, i.e., Reliable Surrogate Gap Sharpness-aware (RSGS) minimization. It integrates sharpness-aware perturbation loss optimization with surrogate gap minimization, while employing entropy-based uncertainty filtering to discard unreliable samples. This mechanism ensures robust distribution alignment and mitigates generalization degradation caused by uncertainties. Thus, CSFA establishes a unified framework for stable adaptation to evolving semantics and distributions in open-world streaming scenarios. Extensive experiments validate the superior performance and effectiveness of CSFA compared to SOTA approaches.

Concept drift—characterized by the evolving and unpredictable nature of data distributions—is a persistent challenge for long-term modeling in non-stationary environments. In multi-stream scenarios, this problem is further complicated by the dynamic and often entangled dependencies across streams, which cannot be effectively handled by traditional single-stream adaptation methods. To address these challenges, we propose a Multi-scale Adaptive Convolutional Graph framework for multi stream concept drift, called MACG. Our framework represents the dynamic inter-dependencies between streams through a flexible and efficient graph structure, driving stable, long-term multi step prediction tasks across multiple streams. In MACG, conventional reliance on pre-defined graphs is replaced by a multi-scale adaptive convolutional graph learned directly from historical data. By considering multiple time scales, this adaptive structure is able to capture richer and more complex spatial–temporal dependencies, thereby overcoming the limitations of static graphs and achieving high generalizability to unknown or evolving data distributions. During online testing, the model adaptively updates its graph structure and proactively predicts drift by identifying structurally analogous patterns between current drift events and historical drift patterns. This joint design effectively mitigates catastrophic forgetting by leveraging both the recurrence and learnability of drift patterns. Rather than merely repeating past responses, the adaptive graph enables the model to generalize transferable patterns and dynamically update dependencies in drift-affected regions, ensuring that adaptations are both context aware and responsive to new changes. Extensive experiments on four large-scale real-world datasets show that MACG consistently outperforms state-of-the-art baselines in prediction accuracy.

Out-of-distribution (OOD) detection has emerged as a pivotal approach for enhancing the reliability of machine learning models, considering the potential for test data to be sampled from classes disparate from in-distribution (ID) data employed during model training. Detecting those OOD data is typically realized as a distance measurement problem, where those deviating far away from the training distribution in the learned feature space are considered OOD samples. Advanced works have shown great success in learning with prototypes for feature-based OOD detection methods, where each ID class is represented with single or multiple prototypes. However, modeling with a finite number of prototypes would fail to maximally capture intra-class variations. In view of this, this paper extends the existing prototype-based learning paradigm to an infinite setting. This motivates us to design two feasible formulations for the Distributional Prototype Learning (DPL) objective, where, to avoid intractable computation and exploding parameters caused by the infinity nature, our key idea is to model an infinite number of discrete prototypes of each ID class with a class-wise continuous distribution. We theoretically analyze both alternatives, identifying the more stable-converging version of the learning objective. We show that, by sampling prototypes from a mixture of class-conditioned Gaussian distributions, the objective can be efficiently computed in a closed form without resorting to the computationally expensive Monte-Carlo approximation of the involved expectation terms. Extensive evaluations across mainstream OOD detection benchmarks empirically manifest that our proposed DPL has established a new state-of-the-art in various OOD settings.

We study the machine unlearning problem which aims to remove specific training data from a pre-trained machine learning model to allow users to exercise their 'right to be forgotten' to protect user privacy. Conventional machine unlearning methods would degrade the model performance after the unlearning procedure. To mitigate the issue, they typically rely on the access to the remaining training data to fine-tune the unlearned model to mitigate the influence of unlearning. However, accessing the remaining training data may not always be practical for different reasons (e.g., data expiration policies, storage limitations, or additional privacy constraints). Machine unlearning without access to the remaining training data poses significant challenges to retaining model performance. In this paper, we study how to unlearn specific training data from a pre-trained model without accessing the remaining training data and protect model performance without dramatically changing the model's parameters. We propose a practical method called Targeted Label Noise Injection. Intuitively, our method assigns incorrect yet controllable labels to the examples that need to be forgotten and fine-tunes the pre-trained model to learn these new labels. This strategy effectively moves the to-be-forgotten examples across the decision boundary with a small impact on the model's overall performance. We theoretically prove the effectiveness of the proposed method and empirically show that it achieves state-of-the-art unlearning performance across various datasets.

Out-of-distribution (OOD) detection is crucial in modern deep learning applications, as it can identify OOD data drawn from distributions differing from those of the in-distribution (ID) data. Advanced OOD detection methods primarily rely on post-hoc strategies, which identify OOD data by analyzing the predictions of a model well-trained on ID data. However, deep models are known to be impacted by spurious features such as backgrounds, causing existing OOD detection methods to fail in identifying OOD data that share the same spurious features as ID data. Therefore, this paper studies how to mitigate spurious features to improve OOD detection. To address this challenge, we propose a novel method called N on- s emantic E xploration OOD D etection (NsED), which focuses on exploring and exploiting non-semantic features. In particular, NsED first explores non-semantic features in an OOD generalization manner. These non-semantic features are then used to train deep models to be more robust against spurious features. Through extensive experiments on representative benchmarks, we show that NsED significantly and consistently improves the detection performance of many representative post-hoc OOD detection methods. • NsED is proposed to address spurious correlation in OOD detection. • This paper first explores the link between OOD generalization and detection. • Experiments show NsED is robust and its components improve OOD detection.

Cross-domain recommender systems, which are designed to address issues with data sparsity, tend to suffer notable challenges with safeguarding user privacy. While existing cross-domain recommendation methods incorporate privacy mechanisms, they often fall short in practice, offering only one-sided benefits and limited privacy safeguards. In this study, we propose a novel privacy-preserving cross-domain recommender system that combines federated transfer learning with differential privacy to facilitate cross-domain knowledge transfer while ensuring strong privacy protection. First, we leverage federated transfer learning, treating each domain as an independent client to protect privacy for business partners by preventing the exchange of raw data. Second, we use a graph neural network (GNN) as the encoder to learn the user and item representations. We also design a consistency loss function that maintains the invariance between local and global user representations while preventing representation collapse. Third, we introduce a privacy mechanism that applies differential privacy to the output of each aggregation layer in the GNN—the aim being to protect transferred user representations while balancing privacy with accuracy. Finally, our transfer mechanism operates without user-identifying information, establishing connections between domains by detecting latent overlapping users and subsequently performing personalized preference aggregation. This allows for efficient knowledge transfer across domains. Experiments on real-world datasets show that our approach significantly enhances recommendation accuracy while offering robust privacy protection, outperforming state-of-the-art baselines.

Vision-language models, such as the Contrastive Language-Image Pre-Training (CLIP) model, have achieved significant success in image classification tasks. CLIP demonstrates high expressive power in few-shot learning scenarios due to its pairing of text and image encoders. However, CLIP still faces over-fitting when trained with a limited number of samples. To mitigate this, image augmentation techniques have been proposed in few-shot learning tasks to prevent over-fitting by enriching the dataset. Existing image augmentation methods, primarily designed for single-modal image models, focus solely on transformations within the image itself. However, for CLIP, merely increasing visual variety without considering textual content can reduce generalization ability and may even mislead the model. To address this issue, we introduce a novel image augmentation approach—Integrated Image-Text Augmentation (ITA)— for CLIP model in few-shot learning tasks. This method generates new and diverse augmented images to increase the diversity of the training data and reduce over-fitting. Additionally, ITA establishes an alignment between the augmented images and their textual descriptions. Through this alignment, the model not only learns to recognize visual elements in the images but also understands the semantic connections between these elements and the text descriptions. This dual-modal approach enhances the model’s flexibility and accuracy in processing few-shot learning tasks. Extensive experiments in few-shot image classification scenarios have demonstrated that ITA shows significant improvements compared to various image augmentation techniques.

Recommender systems have become important tools in the daily life of human beings since they are powerful to address information overload, and discover relevant and useful items for users. The success of recommender systems largely relies on the interaction history between users and items, which is expected to accurately reflect the preferences of users on items. However, the expectation is easily broken in practice, due to the corruptions made in the interaction history, resulting in unreliable and untrusted recommender systems. Previous works either ignore this issue (assume that the interaction history is precise) or are limited to handling additive noise. Motivated by this, in this paper, we study rating flip noise which widely exists in the interaction history of recommender systems and combat it by modelling the noise generation process. Specifically, the rating flip noise allows a rating to be flipped to any other ratings within the given rating set, which reflects various real-world situations of rating corruption, e.g., a user may randomly click a rating from the rating set and then submit it. The noise generation process is modelled by the noise transition matrix that denotes the probabilities of a clean rating flip into a noisy rating. A statistically consistent algorithm is afterwards applied with the estimated transition matrix to learn a robust recommender system against rating flip noise. Comprehensive experiments on multiple benchmarks confirm the superiority of our method.

Model-based reinforcement learning (RL) aims to learn the underlying dynamics of a given environment. The success of most existing works is built on the critical assumption that the dynamic is fixed, which is unrealistic in many open-world scenarios, such as drone delivery and online chatting, where agents may need to deal with environments with unpredictable changing dynamics (hereafter,real non-stationary environment). Therefore, learning changing dynamics in a real non-stationary environment offers both significant benefits and challenges. This paper proposes a new model-based reinforcement learning algorithm that proactively and dynamically detects possible changes and Learns these Latent and Changing Dynamics (LLCD) in a latent Markovian space for real non-stationary environments. To ensure the Markovian property of the RL model and improve computational efficiency, we employ a latent space model to learn the environment’s transition dynamics. Furthermore, we perform online change detection in the latent space to promptly identify change points in non-stationary environments. Then, we utilize the detected information to help the agent adapt to new conditions. Experiments indicate that the rewards of the proposed algorithm accumulate for the most rapid adaptions to environmental change, among other benefits. This work has a strong potential to enhance environmentally suitable model-based reinforcement learning capabilities.

Concept drift arises from unpredictable data distribution shifts, degrading model performance. In evolving multiple data streams, these drifts pose greater challenges due to dynamic changes and uncertain inter-stream correlations, demanding robust accuracy and generalization. To address this issue, in this article, we propose a novel multiple data stream learning method, called the adaptive information fusion-based concept drift learning (AIF-CD) method, to adaptively handle multiple data streams with heterogeneous feature spaces and complex drift situations. First, a real-time learning method with a cooperation scheme is proposed to handle multiple data streams. Second, an information fusion-based augmentation process is designed to help enhance the learning efficiency of each stream. Next, a drift severity identification-based adaptation strategy and a process to selectively use the previous timestamps' data are introduced to enhance learning robustness in both synchronous and asynchronous scenarios. Moreover, a detailed runtime complexity and theoretical analysis further explains the learning efficiency of our method. Our key innovation combines real-time adaptation with theoretical guarantees for complex, evolving multi-stream learning. The experiment results in various scenarios under synchronous and asynchronous settings show that the proposed method is more efficient than other benchmark methods.

Cancer risk prediction is a cornerstone of personalized medicine that offers opportunities for early detection and preventive interventions. However, the current models are designed to predict cancer risk face several challenges. First, most rely on traditional statistical methods, which struggle to capture the complexity of genetic, family medical history, and lifestyle factors. Hence, the accuracy of these models is limited. Additionally, the models neglect to integrate multidimensional data sources, particularly genetic information like single nucleotide polymorphisms (SNPs), which could enhance prediction accuracy. Third, while the system might effectively predict risk, it cannot translate those predictions into actionable healthcare recommendations to reduce cancer risk. In this study, we address all three of these limitations. With a focus on six prevalent cancers—we extracted SNP data from the UK Biobank and designed a novel risk prediction model for cancer and personalized healthcare recommendations based upon the mixture of experts (MoE) paradigm and large language models (LLMs), respectively. Named MoE-HRS, experts based two router networks for separate processing by the Transformer and the convolutional neural network (CNN). Experiments on UK Biobank data show that our model outperforms state-of-the-art cancer risk prediction models. To bridge the gap between risk prediction and practical healthcare applications, we devised a healthcare recommender system powered by LLMs. This approach holds promise for enhancing early detection rates and promoting preventive healthcare management (relevant coding and data are available at https://github.com/bjtu-lucas-nlp/MoE-HRS ).

Recommender systems are one of the most successful applications of using AI for providing personalized e-services to customers. However, data sparsity is presenting enormous challenges that are hindering the further development of advanced recommender systems. Although cross-domain recommendation partly overcomes data sparsity by transferring knowledge from a source domain with relatively dense data to augment data in the target domain, the current methods do not handle heterogeneous data very well. For example, using today’s cross-domain transfer learning schemes with data comprising clicks, ratings, user reviews, item metadata, and knowledge graphs will likely result in a poorly performing model. User preferences will not be comprehensively profiled, and accurate recommendations will not be generated. To solve these three challenges—handling heterogeneous data, avoiding negative transfer, and dealing with data sparsity—we designed a new end-to-end deep A dversarial M ulti-channel T ransfer network for C ross- D omain R ecommendation named AMT-CDR . Heterogeneous data is handled by constructing a cross-domain graph based on real-world knowledge graphs—we used Freebase and YAGO. Negative transfer is prevented through an adversarial learning strategy that maintains consistency across the different data channels. Data sparsity is addressed with an end-to-end neural network that considers data across multiple channels and generates accurate recommendations by leveraging knowledge from both the source and target domains. Extensive experiments on three dual-target cross-domain recommendation tasks demonstrate the superiority of AMT-CDR compared to eight state-of-the-art methods. All source code is available at https://github.com/bjtu-lucas-nlp/AMT-CDR .

Concept drift is a phenomenon that the distribution of data streams changes with time. When this happens, model predictions become less accurate. Hence, concept drift needs to be detected and adapted. Existing drift detection methods are good at determining when drift has occurred, but few retrieve information about how the drift came to be present in the stream, i.e., what type of drift has occurred. Hence, discussing the impact of the type of drift on adaptation is a difficult thing. To fill this gap, we propose a pre-trained framework for training a drift detector called a type-driven lite concept drift detector (Type-LDD) that retrieves information about both when and how a drift has occurred. In our proposed pre-trained framework, the Type-LDD including a drift-type identifier and a drift-point locator was based on a synthetic dataset containing a range of drift types. When repurposing the pre-trained model for detecting new data streams, a knowledge distillation module fine-tunes the proposed Type-LDD to speed up inference and keep detection accuracy. The proposed Type-LDD is validated on both synthetic data and real-world data, and demonstrated that accurately identifying the type of drift that has occurred can improve adaptation accuracy.

Concept drift is an inevitable problem in non-stationary data stream environments, due to changes in data distribution over time. In practical applications, multi-stream data is more common and complex than single-stream data, yet they have received little attention. Addressing the concept drift problem while mining correlations between data streams has become a significant challenge. Several research works focus on capturing the correlation between streams using graph neural networks (GNNs), which provides valuable insights. However, these methods fix the correlation graph structure after training and are unable to adapt to the new data distribution with dynamic correlations during testing. To bridge this gap, we propose a novel concept drift self-adaptation framework based on dynamic graph regularization for multi-stream, named Multi-stream Self-adaptation based on Graph Regularization (MSGR). A new graph neural network architecture is proposed to capture deep spatio-temporal correlations and learn a correlation graph structure without any pre-defined graphs. Each node on the graph represents a stream. The correlation graph structure is constructed through Gumbel sampling and an adaptive matrix from the perspective of stream pairs. Thus we attain a high-performance GNN as the base prediction model for the multi-stream multi-step prediction task in the testing stage. To adapt to the new data distribution, we design a self-adaptation mechanism performed by assigning dynamic learning weight for newly arriving samples. Intuitively, we should assign larger learning weights for relevant samples when drift occurs. The self-adaptation process is accomplished by the sub-graph updating and the proposed graph regularization. Error-based drift detection is integrated into the framework. When drift is detected, the weight for sub-graph updating is increased by adjusting the regularization coefficient. In this way, regardless of the type and degree of concept drift occurring on one or more streams, MSGR can achieve high self-adaptation performance and provide accurate prediction results consistently. The comprehensive testing results on both real-world and synthetic datasets show that MSGR can achieve state-of-the-art performance.

Abstract Predicting a researcher's knowledge trajectories beyond their current foci can leverage potential inter‐/cross‐/multi‐disciplinary interactions to achieve exploratory innovation. In this study, we present a method of diffusion‐based network analytics for knowledge trajectory recommendation. The method begins by constructing a heterogeneous bibliometric network consisting of a co‐topic layer and a co‐authorship layer. A novel link prediction approach with a diffusion strategy is then used to capture the interactions between social elements (e.g., collaboration) and knowledge elements (e.g., technological similarity) in the process of exploratory innovation. This diffusion strategy differentiates the interactions occurring among homogeneous and heterogeneous nodes in the heterogeneous bibliometric network and weights the strengths of these interactions. Two sets of experiments—one with a local dataset and the other with a global dataset—demonstrate that the proposed method is prior to 10 selected baselines in link prediction, recommender systems, and upstream graph representation learning. A case study recommending knowledge trajectories of information scientists with topical hierarchy and explainable mediators reveals the proposed method's reliability and potential practical uses in broad scenarios.

Unsupervised domain adaptation leverages the previously gained knowledge from a labeled source domain to tackle the task from a different but similar unlabeled target domain. Most existing methods focus on transferring knowledge from a single source domain, but the information from a single domain may be inadequate to complete the target task. Some previous studies have turned to multi-view representations to enrich the transferable information. However, they simply concatenate multi-view features, which may result in information redundancy. In this paper, we propose a dynamic classifier alignment (DCA) method for multi-source domain adaptation, which aligns classifiers driven from multi-view features via a sample-wise automatic way. As proposed, both the importance of each view and the contribution of each source domain are investigated. To determine the important degrees of multiple views, an importance learning function is built by generating an auxiliary classifier. To learn the source combination parameters, a domain discriminator is developed to estimate the probability of a sample belonging to multiple source domains. Meanwhile, a self-training strategy is proposed to enhance the cross-domain ability of source classifiers with the assistance of pseudo target labels. Experiments on real-world visual datasets show the superiority of the proposed DCA.

Data streams may encounter data distribution changes, which can significantly impair the accuracy of models. Concept drift detection tracks data distribution changes and signals when to update models. Many drift detection methods apply thresholds to distinguish between drift or non-drift streams and to claim their method outperforms others with non-aligned drift thresholds. We consider that selecting a proper drift threshold could be more important than developing a new drift detection algorithm, and different drift detection algorithms may end up with very similar performance with aligned drift thresholds. To better understand this process, we propose a novel threshold selection algorithm to align the drift thresholds of a set of algorithms so that they are all at the same sensitivity level. Based on comprehensive experiment evaluations, we observed that several state-of-the-art drift detection algorithms could achieve similar results by aligning their thresholds, providing a novel insight to explain how drift detection algorithms contribute to data stream learning. We noticed that a higher detection sensitivity improves accuracy for data streams with frequent distribution change. The evaluation results are showing that drift thresholds should not be fixed during stream learning. Rather, they should adjust dynamically based on the prevailing conditions of the data stream.

Data sparsity is a common issue for most recommender systems and can severely degrade the usefulness of a system. One of the most successful solutions to this problem has been cross-domain recommender systems. These frameworks supplement the sparse data of the target domain with knowledge transferred from a source domain rich with data that is in some way related. However, there are three challenges that, if overcome, could significantly improve the quality and accuracy of cross-domain recommendation: 1) ensuring latent feature spaces of the users and items are both maximally matched; 2) taking consideration of user-item relationship and their interaction in modelling user preference; 3) enabling a two-way cross-domain recommendation that both the source and the target domains benefit from a knowledge exchange. Hence, in this paper, we propose a novel deep neural network called Dual Adversarial network for Cross-Domain Recommendation (DA-CDR). By training the shared encoders with a domain discriminator via dual adversarial learning, the latent feature spaces for both the users and items are maximally matched between the source and target domains. The domain-specific encoders are applied with an orthogonal constraint to ensure that any domain-specific features are properly extracted and work as supplement to the shared features. Allowing the two domains to collaboratively benefit from each other results in better recommendations for both domains. Extensive experiments with real-world datasets on six tasks demonstrate that DA-CDR significantly outperforms seven state-of-the-art baselines in terms of recommendation accuracy.

Concept drift is the phenomenon where the data distribution in a data stream changes over time. It is a ubiquitous problem in the real-world, for example, a traffic accident would cause a jam in a certain period, leading to a distribution change in traffic speed. Most research in the concept drift field focuses on single data stream, however, few of them consider multi-stream environments which are more in line with the application needs. To fill this gap, we propose a multi-stream prediction setting and a multi-stream concept drift self-adaptation framework using graph neural network, named SAGN. In SAGN, we reconsider the learning procedure of GNN-based predictors from an aspect of concept drift adaptation for multi-stream. By this design, the prediction task is converted into online streaming data tasks in sub-graphs. Each sub-graph corresponds to an adaptation target and will be updated over time. In this way, locally we can overcome drift in each sub-graph by a designed adaptation technique, and globally the correlation between different data streams is well-preserved as a graph structure. Therefore, whether drift occurs or not, in one or several streams, SAGN can provide consistently accurate prediction results. We comprehensively tested SAGN on both synthetic and real-world, drift and non-drift data in the multi-step prediction task. The experiment results show that SAGN is able to achieve state-of-the-art performance in most cases.

Causal inference from observational data lies at the heart of education, healthcare, optimal resource allocation and many other decision-making processes. Most of existing methods estimate the target treatment effect indirectly by inferring the underlying treatment response functions or the unobserved counterfactual outcome for every individual. These learning methods are subject to issues of model misspecification and high variability. As a complement of existing indirect learning methods, we propose a direct learning framework, called HTENet, for causal inference using deep multi-task learning. It is based on a novel empirical -risk for learning the causal effect model of direct interest in a supervised learning scheme. In our proposed framework, the target treatment effect model is parametrized as a neural network and learned jointly with other auxiliary models in an end-to-end manner. Moreover, we extend the nave HTENet into other two variants, HTENet-Simple and HTENet-Reg, by further incorporating shared representation learning layers and a propensity prediction regularizer. Experiments on simulated and real data demonstrate that the performances of the proposed methods match or are better than that of existing state-of-arts. Moreover, by learning the target treatment effect function directly, the proposed methods tend to obtain more stable estimates than existing methods.

Since support vector regression (SVR) is a flexible regression algorithm, its computational complexity does not depend on the dimensionality of the input space, and it has excellent generalization capability. However, a central assumption with SVRs is that all the required data is available at the time of construction, which means these algorithms cannot be used with data streams. Incremental SVR has been offered as a potential solution, but its accuracy suffers with noise and learning speeds are slow. To overcome these two limitations, we propose a novel incremental regression algorithm, called online robust support vector regression (ORSVR). ORSVR solves nonparallel bound functions simultaneously. Hence, the large quadratic programming problem (QPP) in classical v-SVR are decomposed into two smaller QPPs. An incremental learning algorithm then solves each QPP step-by-step. The results of a series of comparative experiments demonstrate that the ORSVR algorithm efficiently solves regression problems in data streams, with or without noise, and speeds up the learning process.

Entity candidate retrieval plays a critical role in cross-lingual entity linking (XEL). In XEL, entity candidate retrieval needs to retrieve a list of plausible candidate entities from a large knowledge graph in a target language given a piece of text in a sentence or question, namely a mention, in a source language. Existing works mainly fall into two categories: lexicon-based and semantic-based approaches. The lexicon-based approach usually creates cross-lingual and mention-entity lexicons, which is effective but relies heavily on bilingual resources (e.g. inter-language links in Wikipedia). The semantic-based approach maps mentions and entities in different languages to a unified embedding space, which reduces dependence on large-scale bilingual dictionaries. However, its effectiveness is limited by the representation capacity of fixed-length vectors. In this paper, we propose a pivot-based approach which inherits the advantages of the aforementioned two approaches while avoiding their limitations. It takes an intermediary set of plausible target-language mentions as pivots to bridge the two types of gaps: cross-lingual gap and mention-entity gap. Specifically, it first converts mentions in the source language into an intermediary set of plausible mentions in the target language by cross-lingual semantic retrieval and a selective mechanism, and then retrieves candidate entities based on the generated mentions by lexical retrieval. The proposed approach only relies on a small bilingual word dictionary, and fully exploits the benefits of both lexical and semantic matching. Experimental results on two challenging cross-lingual entity linking datasets spanning over 11 languages show that the pivot-based approach outperforms both the lexicon-based and semantic-based approach by a large margin.

Online data stream mining is of great significance in practice because of its ubiquity in many real-world scenarios, especially in the big data era. Traditional data mining algorithms cannot be directly applied to data streams due to (1) the possible change of underlying data distribution over time (i.e., concept drift ) and (2) delayed, short, or even no labels for streaming data in practice. A new research area, named unsupervised concept drift detection , has emerged to tackle this difficulty mainly based on two-sample hypothesis tests, such as the Kolmogorov–Smirnov test. However, it is surprising that none of the existing methods in this area exploit the Bayesian nonparametric hypothesis test, which has clear interpretability and straightforward prior knowledge encoding ability and no strict or unrealistic requirement of prefixing the form for the underlying data distribution. In this article, we present a Bayesian nonparametric unsupervised concept drift detection method based on the Polya tree hypothesis test. The basic idea is to decompose the underlying data distribution into a multi-resolution representation that transforms the whole distribution hypothesis test into recursive and simple binomial tests. Also, an incremental mechanism is especially designed to improve its efficiency in the stream setting. The method effectively detect drifts, and it also locates where a drift happens and the posteriors of hypotheses. The experiments on synthetic data verify the desired properties of the proposed method, and the experiments on real-world data show the better performance of the method for data stream mining compared with its frequentist counterpart in the literature.

Estimating causal effects by making causal inferences from observational data is common practice in scientific studies, business decision-making, and daily life. In today’s data-driven world, causal inference has become a key part of the evaluation process for many purposes, such as examining the effects of medicine or the impact of an economic policy on society. However, although the literature contains some excellent models, there is room to improve their representation power and their ability to capture complex relationships. For these reasons, we propose a novel prior called Causal DP and a model called CDP. The prior captures the complex relationships between covariates, treatments, and outcomes in observational data using a rational probabilistic dependency structure. The model is Bayesian, nonparametric, and generative and is not based on the assumption of any parametric distribution. CDP is designed to estimate various kinds of causal effects—average, conditional average, average treated, quantile, and so on. It performs well with missing covariates and does not suffer from overfitting. Comparative experiments on synthetic datasets against several state-of-the-art methods demonstrate that CDP has a superior ability to capture complex relationships. Further, a simple evaluation to infer the effect of a job training program on trainee earnings from real-world data shows that CDP is both effective and useful for causal inference.

Transferring knowledge across many streaming processes remains an uncharted territory in the existing literature and features unique characteristics: no labelled instance of the target domain, covariate shift of source and target domain, different period of drifts in the source and target domains. Autonomous transfer learning (ATL) is proposed in this paper as a flexible deep learning approach for the online unsupervised transfer learning problem across many streaming processes. ATL offers an online domain adaptation strategy via the generative and discriminative phases coupled with the KL divergence based optimization strategy to produce a domain invariant network while putting forward an elastic network structure. It automatically evolves its network structure from scratch with/without the presence of ground truth to overcome independent concept drifts in the source and target domain. Rigorous numerical evaluation has been conducted along with comparison against recently published works. ATL demonstrates improved performance while showing significantly faster training speed than its counterparts.

Causal inference from observational data aims to estimate causal effects when controlled experimentation is not feasible, but it faces challenges when unobserved confounders exist. The instrumental variable method resolves this problem by introducing a variable that is correlated with the treatment and affects the outcome only through the treatment. However, existing instrumental variable methods require two stages to separately estimate the conditional treatment distribution and the outcome generating function, which is not sufficiently effective. This paper presents a one-stage approach to jointly estimate the treatment distribution and the outcome generating function through a cleverly designed deep neural network structure. This study is the first to merge the two stages to leverage the outcome to the treatment distribution estimation. Further, the new deep neural network architecture is designed with two strategies (i.e., shared and separate) of learning a confounder representation account for different observational data. Such network architecture can unveil complex relationships between confounders, treatments, and outcomes. Experimental results show that our proposed method outperforms the state-of-the-art methods. It has a wide range of applications, from medical treatment design to policy making, population regulation and beyond.

Weather forecasting is usually solved through numerical weather prediction (NWP), which can sometimes lead to unsatisfactory performance due to inappropriate setting of the initial states. In this paper, we design a data-driven method augmented by an effective information fusion mechanism to learn from historical data that incorporates prior knowledge from NWP. We cast the weather forecasting problem as an end-to-end deep learning problem and solve it by proposing a novel negative log-likelihood error (NLE) loss function. A notable advantage of our proposed method is that it simultaneously implements single-value forecasting and uncertainty quantification, which we refer to as deep uncertainty quantification (DUQ). Efficient deep ensemble strategies are also explored to further improve performance. This new approach was evaluated on a public dataset collected from weather stations in Beijing, China. Experimental results demonstrate that the proposed NLE loss significantly improves generalization compared to mean squared error (MSE) loss and mean absolute error (MAE) loss. Compared with NWP, this approach significantly improves accuracy by 47.76%, which is a state-of-the-art result on this benchmark dataset.

With the increasing of online shopping services, fashion recommendation plays an important role in daily online shopping scenes. A lot of recommender systems have been developed with visual information. However, few works take into account compatibility relationship when they are generating recommendations. The challenge is that fashion concept is often subtle and subjective for different customers. In this paper, we propose a fashion compatibility knowledge learning method that incorporates visual compatibility relationships as well as style information. We also propose a fashion recommendation method with domain adaptation strategy to alleviate the distribution gap between the items in target domain and the items of external compatible outfits. Our results indicate that the proposed method is capable of learning visual compatibility knowledge and outperforms all the baselines.

Concept drift describes unforeseeable changes in the underlying distribution of streaming data overtime. Concept drift research involves the development of methodologies and techniques for drift detection, understanding, and adaptation. Data analysis has revealed that machine learning in a concept drift environment will result in poor learning results if the drift is not addressed. To help researchers identify which research topics are significant and how to apply related techniques in data analysis tasks, it is necessary that a high quality, instructive review of current research developments and trends in the concept drift field is conducted. In addition, due to the rapid development of concept drift in recent years, the methodologies of learning under concept drift have become noticeably systematic, unveiling a framework which has not been mentioned in literature. This paper reviews over 130 high quality publications in concept drift related research areas, analyzes up-to-date developments in methodologies and techniques, and establishes a framework of learning under concept drift including three main components: concept drift detection, concept drift understanding, and concept drift adaptation. This paper lists and discusses 10 popular synthetic datasets and 14 publicly available benchmark datasets used for evaluating the performance of learning algorithms aiming at handling concept drift. Also, concept drift related research directions are covered and discussed. By providing state-of-the-art knowledge, this survey will directly support researchers in their understanding of research developments in the field of learning under concept drift.

Whereas traditional science maps emphasize citation statistics and static relationships, this paper presents a term‐based method to identify and visualize the evolutionary pathways of scientific topics in a series of time slices. First, we create a data preprocessing model for accurate term cleaning, consolidating, and clustering. Then we construct a simulated data streaming function and introduce a learning process to train a relationship identification function to adapt to changing environments in real time, where relationships of topic evolution, fusion, death, and novelty are identified. The main result of the method is a map of scientific evolutionary pathways. The visual routines provide a way to indicate the interactions among scientific subjects and a version in a series of time slices helps further illustrate such evolutionary pathways in detail. The detailed outline offers sufficient statistical information to delve into scientific topics and routines and then helps address meaningful insights with the assistance of expert knowledge. This empirical study focuses on scientific proposals granted by the United States National Science Foundation, and demonstrates the feasibility and reliability. Our method could be widely applied to a range of science, technology, and innovation policy research, and offer insight into the evolutionary pathways of scientific activities.

Traditional relational topic models provide a successful way to discover the hidden topics from a document network. Many theoretical and practical tasks, such as dimensional reduction, document clustering, and link prediction, could benefit from this revealed knowledge. However, existing relational topic models are based on an assumption that the number of hidden topics is known a priori, which is impractical in many real-world applications. Therefore, in order to relax this assumption, we propose a nonparametric relational topic model using stochastic processes instead of fixed-dimensional probability distributions in this paper. Specifically, each document is assigned a Gamma process, which represents the topic interest of this document. Although this method provides an elegant solution, it brings additional challenges when mathematically modeling the inherent network structure of typical document network, i.e., two spatially closer documents tend to have more similar topics. Furthermore, we require that the topics are shared by all the documents. In order to resolve these challenges, we use a subsampling strategy to assign each document a different Gamma process from the global Gamma process, and the subsampling probabilities of documents are assigned with a Markov Random Field constraint that inherits the document network structure. Through the designed posterior inference algorithm, we can discover the hidden topics and its number simultaneously. Experimental results on both synthetic and real-world network datasets demonstrate the capabilities of learning the hidden topics and, more importantly, the number of topics.

The evolution potential estimation of news events can support the decision making of both corporations and governments. For example, a corporation could manage its public relations crisis in a timely manner if a negative news event about this corporation is known with large evolution potential in advance. However, existing state-of-the-art methods are mainly based on time series historical data, which are not suitable for the news events with limited historical data and bursty properties. In this article, we propose a purely content-based method to estimate the evolution potential of the news events. The proposed method considers a news event at a given time point as a system composed of different keywords, and the uncertainty of this system is defined and measured as the Semantic Uncertainty of this news event. At the same time, an uncertainty space is constructed with two extreme states: the most uncertain state and the most certain state. We believe that the Semantic Uncertainty has correlation with the content evolution of the news events, so it can be used to estimate the evolution potential of the news events. In order to verify the proposed method, we present detailed experimental setups and results measuring the correlation of the Semantic Uncertainty with the Content Change of news events using collected news events data. The results show that the correlation does exist and is stronger than the correlation of value from the time-series-based method with the Content Change. Therefore, we can use the Semantic Uncertainty to estimate the evolution potential of news events.

Incorporating the side information of text corpus, i.e., authors, time stamps, and emotional tags, into the traditional text mining models has gained significant interests in the area of information retrieval, statistical natural language processing, and machine learning. One branch of these works is the so-called Author Topic Model (ATM), which incorporates the authors's interests as side information into the classical topic model. However, the existing ATM needs to predefine the number of topics, which is difficult and inappropriate in many real-world settings. In this paper, we propose an Infinite Author Topic (IAT) model to resolve this issue. Instead of assigning a discrete probability on fixed number of topics, we use a stochastic process to determine the number of topics from the data itself. To be specific, we extend a gamma-negative binomial process to three levels in orderto capture the author-document-keyword hierarchical structure. Furthermore, each document is assigned a mixed gamma process that accounts for the multi-author's contribution towards this document. An efficient Gibbs sampling inference algorithm witheach conditional distribution being closed-form is developed for the IAT model. Experiments on several real-world datasets show the capabilities of our IAT model to learn the hidden topics, authors' interests on these topics and the number of topics simultaneously.

With the extraordinary spread of computers and sensors, enormous amounts of data are generated every day in a range of areas-search engines, social media, healthcare organizations, insurance companies, financial industry, retail, and many others.Data science refers to the theories, methods, and applications for extracting previously unavailable and potentially highly useful information from data.This field has evolved as a hybrid of research in data mining, machine learning, computational intelligence, databases, algorithms, statistics, operations research, visualization, privacy, and security.It is helping us make sense out of vast quantities of information.However, how to use these data by an effective and ethical way is a significant challenge to science and to society as a whole.Intelligent techniques, including artificial intelligence, neural networks, fuzzy logic, granular computing, rough sets, expert systems, case-based reasoning, evolutionary algorithms, and swarm computing, have been successfully applied in many fields including data science.This special issue is devoted to the use of intelligent techniques for data science that reflects their current development obtained from selected papers submitted to the 8th International Conference on Intelligent Systems and Knowledge Engineering (ISKE2013) held in Shenzhen, People's Republic of China, during November 20-23, 2013.This issue encompasses seven papers that present the application of different intelligent techniques to different data science problems ranging from recommender systems to recognition processes passing by others such as activity simulation, fuzzy trading systems, deep learning, and incremental learning.The paper coauthored by Wei Wang, Guangquan Zhang, and Jie Lu investigates the collaborative filtering with an entropy-driven user similarity in recommender systems.It aims at improving recommendation performance, by means of a novel

Collaborative filtering (CF) is the most popular approach in personalized recommender systems. Although CF approaches have successfully been used and have the advantage in that it is unnecessary to analyze item content when generating recommendations, they nevertheless suffer from problems with accuracy. In this paper, we propose a new CF approach to improve recommendation performance. First, a new information entropy-driven user similarity measure model is proposed to measure the relative difference between ratings. A Manhattan distance-based model is then developed to address the fat tail problem by estimating the alternative active user average rating. The effectiveness of the proposed approach is analyzed on public and private data sets. As a result of the introduction of the new similarity measure and average rating estimation, we demonstrate that the proposed new CF recommendation approach provides better recommendations.

Web-page recommendation plays an important role in intelligent Web systems. Useful knowledge discovery from Web usage data and satisfactory knowledge representation for effective Web-page recommendations are crucial and challenging. This paper proposes a novel method to efficiently provide better Web-page recommendation through semantic-enhancement by integrating the domain and Web usage knowledge of a website. Two new models are proposed to represent the domain knowledge. The first model uses an ontology to represent the domain knowledge. The second model uses one automatically generated semantic network to represent domain terms, Web-pages, and the relations between them. Another new model, the conceptual prediction model, is proposed to automatically generate a semantic network of the semantic Web usage knowledge, which is the integration of domain knowledge and Web usage knowledge. A number of effective queries have been developed to query about these knowledge bases. Based on these queries, a set of recommendation strategies have been proposed to generate Web-page candidates. The recommendation results have been compared with the results obtained from an advanced existing Web Usage Mining (WUM) method. The experimental results demonstrate that the proposed method produces significantly higher performance than the WUM method.

Auctions can be characterized by distinct nature of their feature space. This feature space may include opening price, closing price, average bid rate, bid history, seller and buyer reputation, number of bids and many more. In this paper, a clustering based method is used to forecast the end-price of an online auction for autonomous agent based system. In the proposed model, the input auction space is partitioned into groups of similar auctions by k-means clustering algorithm. The recurrent problem of finding the value of k in k-means algorithm is solved by employing elbow method using one way analysis of variance (ANOVA). Then k numbers of regression models are employed to estimate the forecasted price of an online auction. Based on the transformed data after clustering and the characteristics of the current auction, bid selector nominates the regression model for the current auction whose price is to be forecasted. Our results show the improvements in the end price prediction for each cluster which support in favor of the proposed clustering based model for the bid prediction in the online auction environment.

Recommender systems aim to assist web users to find only relevant information to their needs rather than an undifferentiated mass of information. Collaborative filtering (CF) techniques are probably the most popular and widely adopted techniques in recommender systems. Despite of their success in various applications, CF-based techniques still encounter two major limitations, namely sparsity and cold-start problems. More recently, semantic information of items has been successfully used in recommender systems to alleviate such problems. Moreover, the incorporation of multi-criteria ratings in recommender systems can help to produce more accurate recommendations. Thereby, in this paper, we propose a hybrid Multi-Criteria Semantic-enhanced CF (MC-SeCF) approach. The MC-SeCF approach integrates the enhanced MC item-based CF and the item-based semantic filtering approaches to alleviate current limitations of the item-based CF techniques. Experimental results demonstrate the effectiveness of the proposed MC-SeCF approach in terms of improving accuracy, as well as in dealing with very sparse data sets or cold-start items compared to benchmark item-based CF techniques.

The information overload on the World Wide Web results in the underuse of some existing e-government services within the business domain. Small-to-medium businesses (SMBs), in particular, are seeking “one-to-one'' e-services from government in current highly competitive markets, and there is an imperative need to develop Web personalization techniques to provide business users with information and services specific to their needs, rather than an undifferentiated mass of information. This paper focuses on how e-governments can support businesses on the problem of selecting a trustworthy business partner to perform reliable business transactions. In the business partner selection process, trust or reputation information is crucial and has significant influence on a business user's decision regarding whether or not to do business with other business entities. For this purpose, an intelligent trust-enhanced recommendation approach to provide personalized government-to-business (G2B) e-services, and in particular, business partner recommendation e-services for SMBs is proposed. Accordingly, in this paper, we develop (1) an implicit trust filtering recommendation approach and (2) an enhanced user-based collaborative filtering (CF) recommendation approach. To further exploit the advantages of the two proposed approaches, we develop (3) a hybrid trust-enhanced CF recommendation approach (TeCF) that integrates both the proposed implicit trust filtering and the enhanced user-based CF recommendation approaches. Empirical results demonstrate the effectiveness of the proposed approaches, especially the hybrid TeCF recommendation approach in terms of improving accuracy, as well as in dealing with very sparse data sets and cold-start users. © 2011 Wiley Periodicals, Inc.

E-government is becoming more attentive towards providing intelligent personalized online services to citizens so that citizens can receive better services with less time and effort. This paper proposes a new conceptual framework for delivering personalized e-government services to citizens from a citizen-centric approach, called Pe-Gov service framework. This framework outlines the main components and their interconnections. Detailed explanations about these components are given and the special features of this framework are highlighted. The Pe-Gov framework has the potential to outperform the existing e-Gov service systems as illustrated by two real life examples. © 2010 ACM.

The emerging Semantic Web relies on the development of ontologies and the deployment of data annotated by ontologies. For a certain domain with a suitable ontology developed, its ontology annotated data (or simply ontology data) from different sources is often overlapping. Similar to a data warehousing process that transforms and merges data from different databases, an integration over the Semantic Web data sources needs to match relevant ontology data among them. This study develops a matching method to address the issue of ontology data matching. This method is different from other data matching or merging methods applied to database or data warehouse cleansing in that it employs more similarity measurements by exploring ontology features. Our experiments show that this proposed method increases matching accuracy.

An exploratory study of Web-based cognitive business intelligence systems (CBIS) is presented in this paper. The underpinning concepts and theories are situation awareness, mental model, and naturalistic decision making (NDM). The CBIS is an extension of the traditional business intelligence system with cognitive orientation. It focuses on developing, enriching, and utilizing the executive's situation awareness, mental models, and other past experience during human-computer interaction, which drives the decision process to approach a naturalistic decision.

Information overload is becoming one of the problems that hinder the effectiveness of e-government services. Intelligent e-government services with personalized recommendation techniques can provide a solution for this problem. Existing recommendation approaches have not entirely considered the influences of attributes of various online services and may result in no guarantee of recommendation accuracy. This study proposes a new approach to handle recommendation issues of one-and-only items in e-government services. The proposed approach integrates the techniques of semantic similarity and the traditional item-based collaborative filtering. A recommender system named Smart Trade Exhibition Finder has been developed to implement the proposed recommendation approach. The recommender system can be applied in e-government services to improve the quality of government-to-business online services. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 401–417, 2007.

Electronic-service ~e-service! intelligence is a new research field that deals with fundamental roles, social impacts, and practical applications of various intelligent technologies and methodologies on Internet-based e-services.It has been recently identified as a novel direction and a next stage of e-services for current and future development.E-services involve various types, delivery systems, advanced information technologies, methodologies, and applications of online services that are provided by e-government, e-business, e-commerce, e-market, e-finance, and e-learning systems, to name a few.They thus offer great opportunities and challenges for many areas, such as government, business, commerce, marketing, finance, and education.E-service intelligence comes from real-world requirements, where many government and business organizations are nowadays developing their e-service systems with various intelligent functions.These organizations need to integrate various intelligent technologies and methodologies into their e-service systems for realizing intelligent online services for their users.Over the past decade, in most developed countries both government and business online services have mainly gone through three stages: ~1! online information presentation, ~2! online transaction, and ~3! online information integration.As an example, we take the case of the development of the Australian e-government.At stage one ~1997-2000!the Australian government made information available online and provided simple groupware functionalities such as e-mail queries and

Information integration with the aid of ontology can roughly be divided into two levels: schema level and data level. Most research has been focused on the schema level, i.e., mapping/matching concepts and properties in different ontologies with each other. However, the data level integration is equally important, especially in the decentralized semantic Web environment. Noticing that ontology data (in the form of instances of concepts) from different sources often have different perspectives and may overlap with each other, we develop a matching method that utilizes the features of ontology and employs the machine learning approach to integrate those instances. By exploring ontology features, this method performs better than other general methods, which is revealed in our experiments. Through the process that implements the matching method, ontology data can be integrated together to offer more sophisticated services

Recommender systems have gained successfully applications particular in e-commerce domain. However, existing recommendation approaches can not effectively deal with recommendation issue of one-and-only items occurred in government-to-business services, e.g. recommendation of trade exhibitions. Thus, in this study, we propose a novel approach by integrating semantic information with the traditional item-based collaborative filtering, and attempt to help the businesses choose the right trade exhibitions at the right time. The outcome of this study have tremendous significance in overcoming the 'new item' problem of existing recommendation approaches.

With the fast growth of e-commerce, product reviews on the Web have become an important information source for customers' decision making when they plan to buy products online. As the reviews are often too many for customers to go through, how to automatically classify them into different semantic orientations (i.e. recommend/not recommend) has become a research problem. Different from traditional approaches that treat a review as a whole, our approach performs semantic classifications at the sentence level by realizing reviews often contain mixed feelings or opinions. In this approach, a typical feature selection method based on sentence tagging is employed and a naive Bayes classifier is used to create a base classification model, which is then combined with certain heuristic rules for review sentence classification. Experiments show that this approach achieves better results than using general naive Bayes classifiers.