Zhang / Henry / Jensen Rough Sets

International Joint Conference, IJCRS 2025, Chongqing, China, May 11–13, 2025, Proceedings, Part II
Erscheinungsjahr 2025
ISBN: 978-3-031-92744-7
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 454 Seiten

Reihe: Lecture Notes in Artificial Intelligence

This three-volume set LNAI 15708-15709-15110 constitutes the proceedings of the International Joint Conference on Rough Sets, IJCRS 2025, held in Chongqing, China, during May 11–13, 2025.
The 90 full papers included in these volumes were carefully reviewed and selected from 187 submissions. They are organized in topical sections as follows:
Part I: Rough Set Models and Foundations; Fuzzy Rough Sets and Rough Fuzzy Sets; and Granular Computing.
Part II: Rough Set Applications; Feature Selection and Knowledge Discovery; and Cognitive Computing.
Part III: Three-way Data Analytics and Decision; Medicine and Health Data Mining; and Applications of Deep Learning and Soft Computing.

Zielgruppe

Research

Autoren/Hrsg.

Zhang, Qinghua

Henry, Christopher

Jensen, Richard

Gao, Xinbo

Wang, Guoyin

Yao, JingTao

Cornelis, Chris

Xia, Shuyin

Inhaltsverzeichnis

.- Rough Set Applications.

.- Comparison of Complexity of Regular and Oblivious Decision Trees for Decision Tables from Closed Classes.

.- Applying Rough Set based Feature Selection Method to Spam Classification.

.- What Is Inside Agent Brain: Application of Learning from Examples Using Rough Sets (LEM2) Rule Induction Algorithm to Explain Actions of Vision based Machine Learning Agents Trained with Proximal Policy Optimization.

.- Quick Neighborhood Rough Set for Hierarchical Classification.

.- (?,?*) Operator Driven Intuitionistic Fuzzy Matrix Composite Operation and Its Optimal Application in Medical Diagnosis.

.- User and Item sub contexts Induced Fuzzy Concept Set for Recommendation.

.- Rough Set Theory Applied to Feature Selection.

.- Fusing Land Use Knowledge with Multi granularity Temporal and Spatial Dependencies for Traffic Accident Prediction.

.- A Hybrid Multi attribute Group Decision making Method Using Normal Cloud Model and Multi granularity Information.

.- Anomaly Detection Using Fuzzy Information Entropy for Incomplete Data.

.- Feature Selection and Knowledge Discovery.

.- Feature Selection Based on Cross Neighborhood Granular Ball Layer.

.- A Dynamic Unsupervised Feature Selection Method Based on Information Sets and Fuzzy Rough Sets.

.- Distance guided Pseudo Label Graph Clustering Network.

.- Robust Feature Selection Based on Intuitionistic Hesitant Fuzzy Cross Correlation and Manifold Learning.

.- Adaptive Correlation Incorporated Latent Feature Analysis for Online Sparse Streaming Feature Selection.

.- Accelerated Feature Selection Based on Granular ball Rough Sets.

.- Online Multi label Stream Feature Selection Based on Neighborhood Approximation Error Rate and Label Correlation.

.- Cross view Fuzziness and Intra view Uncertainty based Weight Reconstruction for Multi view Feature Selection.

.- Finding Consistent Pairwise Comparisons with Genetic Algorithms.

.- Multi view Unsupervised Feature Selection Guided by Diversity and Consensus Structure.

.- Cognitive Computing.

.- Legal Similar Case Retrieval Model Based on Concept Tree and Optimal Transport.

.- Enhancing Knowledge Tracing via Random Layer wise Adversarial Training.

.- Incomplete Multi view Clustering Based on Joint Concept Decomposition and Anchor Graph Learning.

.- Frequency sensitive Sparse Transformer with Multi Granularity Refinement Network for Image Restoration.

.- TSKE: A Dual Branch Model for Knowledge Graph Embedding with Joint Textual and Structural Information.

.- Efficient Local Causal Structure Learning with Privacy Preservation.

.- The Connections Between Approximate Three way Concept Lattice and Three way Approximate Concept Lattices.

.- Concept Oriented Attribute Reduction in Three way Concept Analysis.

.- Deterministic and Nondeterministic Decision Trees for Recognition of Properties of Decision Rule Systems.

.- Concept Reduction Method Based on Attribute (Object) Reduction.