-
Article
Debiased graph contrastive learning based on positive and unlabeled learning
Graph contrastive learning (GCL) is one of the mainstream techniques for unsupervised graph representation learning, which reduces the distance between positive pairs and increases the distance between negativ...
-
Article
Re-attentive experience replay in off-policy reinforcement learning
Experience replay, which stores past samples for reuse, has become a fundamental component of off-policy reinforcement learning. Some pioneering works have indicated that prioritization or reweighting of sampl...
-
Article
Robust graph neural networks with Dirichlet regularization and residual connection
Graph Neural Network (GNN) has attracted considerable research interest in various graph data modeling tasks. Most GNNs require efficient and sufficient label information during training phase. However, in ope...
-
Article
Coupling learning for feature selection in categorical data
Feature selection, which is a commonly used data prepossessing technique, focuses on improving model performance and efficiency by removing redundant or irrelevant features. However, an implicit assumption mad...
-
Article
Hybrid sampling-based contrastive learning for imbalanced node classification
Imbalanced node classification is a vital task because it widely exists in many real-world applications, such as financial fraud detection, anti-money laundering, drug reaction prediction and so on. However, m...
-
Article
A trilevel analysis of uncertainty measuresin partition-based granular computing
Uncertainty measure is one of the most significant concepts and fundamental issues in granular computing. Nowadays, there have been extensive studies on various uncertainty measures for quantifying diverse pro...
-
Article
Hierarchical metric learning with intra-level and inter-level regularization
Metric learning for hierarchical classification is a significant problem whose purpose is to learn more discriminative metrics by exploiting the dataset’s hierarchical structure and achieving higher accuracy r...
-
Article
Clustering mixed type data: a space structure-based approach
Clustering mixed type data is important for the areas such as knowledge discovery and machine learning. Although many clustering algorithms have been developed for mixed type data, clustering mixed type data i...
-
Article
An unsupervised multi-manifold discriminant isomap algorithm based on the pairwise constraints
In this paper, an unsupervised multi-manifold Isomap algorithm, which is named UMD-Isomap, is proposed for the purpose of dimensionality reduction and clustering of multi-manifold data. First, the global pairw...
-
Article
A Bayesian matrix factorization model for dynamic user embedding in recommender system
-
Article
Incomplete multi-view clustering via local and global co-regularization
The incompleteness of multi-view data is a phenomenon associated with real-world data mining applications, which brings a huge challenge for multi-view clustering. Although various types of clustering methods,...
-
Article
A group incremental approach for feature selection on hybrid data
Feature selection for dynamic data sets has been perceived as a very significant hot research problem in data mining. In practice, most real-world data usually are hybrid, which means both include categorical ...
-
Article
Accelerating ReliefF using information granulation
Feature selection is an essential preprocessing requirement when solving a classification problem. In this respect, the Relief algorithm and its derivatives have been demonstrated to be a class of successful f...
-
Article
Logic could be learned from images
Logic reasoning is a significant ability of human intelligence and also an important task in artificial intelligence. The existing logic reasoning methods, quite often, need to design some reasoning patterns b...
-
Article
Metric learning with clustering-based constraints
In most of the existing metric learning methods, the relation is fixed throughout the metric learning process. However, the fixed relation may be harmful to learn a good metric. The adversarial metric learning...
-
Article
BIC-based node order learning for improving Bayesian network structure learning
Node order is one of the most important factors in learning the structure of a Bayesian network (BN) for probabilistic reasoning. To improve the BN structure learning, we propose a node order learning algorith...
-
Article
Graph-based semi-supervised learning via improving the quality of the graph dynamically
Graph-based semi-supervised learning (GSSL) is an important paradigm among semi-supervised learning approaches and includes the two processes of graph construction and label inference. In most traditional GSSL...
-
Article
MAGDM-oriented dual hesitant fuzzy multigranulation probabilistic models based on MULTIMOORA
In real world, multi-attribute group decision making (MAGDM) is a complicated cognitive process that involves expression, fusion and analysis of multi-source uncertain information. Among diverse soft computing...
-
Article
A Novel Preference Measure for Multi-Granularity Probabilistic Linguistic Term Sets and its Applications in Large-Scale Group Decision-Making
Comparing probabilistic linguistic term sets (PLTSs) is quite essential in solving PLTS-expressed multi-attribute group decision-making problems (PLTS-MAGDM). Researchers have designed various comparison measu...
-
Article
An accelerator for the logistic regression algorithm based on sampling on-demand
