Rough Sets : International Joint Conference, IJCRS 2024, Halifax, NS, Canada, May 17-20, 2024, Proceedings, Part I

• Autore: Hu Mengjun
• Pubblicazione: Cham : , : Springer International Publishing AG, , 2024; ©2024
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (384 pages)
• Altri autori: CornelisChris ZhangYan LingrasPawan ŚlęzakDominik YaoJingTao
• Nota di contenuto: Intro -- Preface -- Organization -- IRSS President Forum Talks -- The Contributions of Rough Set Theory to Artificial Intelligence -- From Deterministic to Probabilistic Rough Sets -- Keynote Talks -- Analysis of Healthcare Data - Explainability Using Rough Sets and LLMs -- Generative Information Retrieval: RAG and GAR -- On Optimal Approximations for Rough Sets -- Self-organizing Cyber-physical Systems Development with Rough Sets -- Rough Set Theory and Concept Lattices to Solve Fuzzy Relation Equations -- Complex Collective Systems - Examples of Intelligent Particles Interactions -- Contents - Part I -- Contents - Part II -- Rough Set Models and Foundations -- Mapper-Based Rough Sets -- 1 Introduction -- 2 Preliminaries -- 3 Generating Coverings with the Mapper Algorithm -- 3.1 The Mapper Algorithm -- 3.2 A Simple Example -- 3.3 Effect of the Lens Function -- 4 Application on Benchmark Datasets -- 4.1 Iris Dataset -- 4.2 Wine Dataset -- 4.3 Breast Cancer Dataset -- 5 Conclusion and Future Work -- References -- On Tolerance-Based Rough Set Operators and Their Covering Generalizations -- 1 Introduction -- 2 Elements of Rough Set Theory -- 3 Tolerance Rough Sets -- 4 Tolerance-Granular Rough Sets -- 5 Conclusions -- References -- Parametrized -Decision Valuation for Variable Precision Rough Set Model -- 1 Introduction -- 2 VPRS Model -- 3 Decision Valuation and Decision Reduct -- 4 VPRS Model Versus a Parametrized -Decision Valuation -- 5 Properties of Decision Valuation Representing VPRS Model -- 6 Conclusion -- References -- Rough Algebraic Semantics of Concepts in a Distributed Cognition Perspective -- 1 Introduction -- 2 Some Background -- 3 CUD-Approximations -- 4 Pi-Groupoids -- 4.1 Pi-Groupoidal Approximations -- 4.2 Abstract Example -- 5 Pi-Groupoidal Algebraic Semantics -- 6 Interpretation and Directions -- References. ; Description Logic for Rough Concepts -- 1 Introduction -- 2 Preliminaries -- 2.1 Basic Lattice-Based Modal Logic and Its Polarity-Based Semantics -- 2.2 Rough Concepts -- 2.3 Description Logic LE-ALC -- 3 Description Logic for Rough Concepts and Tableaux Algorithm for It -- 3.1 Tableaux Algorithm for LE-ALCR ABoxes -- 4 Soundness -- 5 Completeness -- 6 TBox Consistency and Extensions of LE-ALCR -- 6.1 Tableaux Algorithm for TBox Axioms -- 6.2 Extending LE-ALCR with Generated Concepts -- 6.3 Extending LE-ALCR with Feature-Pair Inconsistencies -- 7 Example -- 8 Conclusion and Future Work -- References -- Rule Induction and Machine Learning -- Greedy Algorithm for Construction of Deterministic Decision Trees for Conventional Decision Tables from Closed Classes -- 1 Introduction -- 2 Main Definitions and Notation -- 2.1 Decision Tables -- 2.2 Deterministic Decision Trees -- 2.3 Complexity Measures -- 2.4 Parameters of Decision Trees and Tables -- 3 Main Results -- 4 Auxiliary Statements -- 5 Proofs of Theorems 2 and 3 -- 6 Conclusions -- References -- Study of Dependency Degree and Bayesian Networks for Conflict Scenarios -- 1 Introduction -- 2 The Dependency Degree of Attributes -- 3 Causal Discovery and Bayesian Networks -- 4 Parliamentary Elections in Poland in 2023 -- 5 Conclusion -- References -- Consideration of Detecting Data and Functional Dependency in Tabular Data with Missing Values by the Obtained Rules -- 1 Introduction and Background -- 2 Detection of Data and Functional Dependency Using the Obtained Rules -- 2.1 Strict Dependency (no Inconsistency) in DIS -- 2.2 Weakened Dependency (with Inconsistency) in DIS -- 2.3 The Worst and the Best Strict Dependency in NIS -- 3 Calculation of the Degree of Dependency -- 4 Dependency for Another Decision Attribute -- 5 Concluding Remarks -- References. ; Distance-Based Fuzzy-Rough Sets and Their Application to the Classification Problem -- 1 Introduction -- 2 Preliminaries -- 2.1 Distances -- 2.2 Fuzzy Set Theory and Fuzzy Logic -- 2.3 Fuzzy-Rough Set Theory -- 3 Distance-Based Fuzzy-Rough Sets -- 4 Classification Problem -- 4.1 Classification as Approximation of Concepts -- 4.2 Crisp and Fuzzy Interpretations of Training Data -- 4.3 Approximating Concepts Using Distance-Based (,) and (S,T) Fuzzy-Rough Sets -- 4.4 Thresholding Property and Region of No Information (RONI) -- 5 Geometry of Approximation -- 5.1 Finite Generators -- 5.2 Infinite Generators -- 6 Naive Classifiers -- 6.1 Decision Regions and SVM -- 7 Experiments -- 8 Conclusion and Future Work -- References -- Dealing with Missing Values Meaning Unknown in Probabilistic Approximations -- 1 Introduction -- 2 Rough Approximations and Probabilistic Approximations -- 3 Probabilistic Approximations in Incomplete Data Tables -- 4 Probabilistic Approximations Based on Possible World Semantics -- 4.1 Possible Tables and Their Indiscernibility Relations -- 4.2 Equivalence Classes in Possible Tables -- 4.3 Aggregation of Indiscernibility Relations in Possible Tables -- 4.4 Lower and Upper Bounds of Probabilistic Approximations -- 5 Conclusions -- References -- On Complexity of Deterministic and Nondeterministic Decision Trees for Decision Tables with Many-Valued Decisions from Closed Classes -- 1 Introduction -- 2 Main Definitions and Notation -- 2.1 Decision Tables -- 2.2 Deterministic and Nondeterministic Decision Trees -- 2.3 Complexity Measures -- 2.4 Parameters of Decision Trees and Tables -- 3 Main Results -- 3.1 Function F,A -- 3.2 Function G,A -- 4 Proofs of Theorems 1 and 2 -- 5 Conclusions -- References -- Simulating Functioning of Decision Trees for Tasks on Decision Rule Systems -- 1 Introduction -- 2 Definitions. ; 2.1 DRSs - Decision Rule Systems -- 2.2 DTs - Decision Trees -- 3 Supporting Statements -- 4 Algorithms -- 4.1 Supporting Algorithm Agreedy -- 4.2 Greedy Algorithm AC, C{SR,ESR,AD,EAD} -- 5 Conclusion and Plans for Future -- References -- RIONIDA: A Novel Algorithm for Imbalanced Data Combining Instance-Based Learning and Rule Induction -- 1 Introduction -- 2 Related Work -- 3 Basic Notions -- 4 The RIONA Algorithm -- 5 The RIONIDA Algorithm -- 6 Estimating the Optimal Values of Parameters for RIONIDA -- 6.1 Efficient Learning of the Optimal Values of Parameters for RIONIDA -- 7 Experiments and Main Results -- 7.1 Comparison of RIONIDA with the Selected State-of-the-Art Algorithms for G-Mean -- 7.2 Comparison of RIONIDA with the Selected State-of-the-Art Algorithms for F-Measure -- 7.3 Conclusions for Experiments -- 8 Conclusions -- References -- Granular Computing -- Information System in the Light of Interactive Granular Computing -- 1 Introduction -- 2 A Brief Idea About IGrC -- 3 Information Systems from the Perspective of IGrC -- 4 Example Illustrating Interactive Information System -- 5 Conclusion -- References -- GBTWSVM: Granular-Ball Twin Support Vector Machine -- 1 Introduction -- 2 Preliminaries -- 2.1 Granular-Ball Computing -- 2.2 TWSVM -- 3 The model of Granular-Ball Twin Support Vector Machine -- 3.1 Granular-Ball Twin Support Vector Machine -- 3.2 The Dual Model of GBTWSVM -- 3.3 Algorithm Design -- 4 Experiment Analysis -- 4.1 Penalty Parameters Selection -- 4.2 Evaluation Metrics -- 4.3 Experimental Datasets -- 4.4 Experimental Results -- 5 Conclusions -- References -- Fuzzy Granular-Balls Based Spectral Clustering -- 1 Introduction -- 2 Preliminaries -- 2.1 Fuzzy C-Means -- 2.2 Spectral Clustering -- 2.3 Granular-Ball Computing -- 3 Fuzzy Granular-Balls Based Spectral Clustering -- 4 Experimental Analysis. ; 4.1 Experimental Datasets and Metric Indexes -- 4.2 The Experimental Results of the FGBSC -- 5 Conclusions -- References -- A Vector Is a Granule: A Novel Extension of the Variable Precision Rough Set Model -- 1 Introduction -- 2 Preliminaries -- 2.1 Pawlak's Rough Set Model -- 2.2 Ziarko's Variable Precision Rough Set Model -- 2.3 Inner Product Spaces -- 3 Granule Vectors and Approximation Vectors -- 3.1 The Case of Using the Standard Basis -- 3.2 The Case of Using Linearly Independent Vectors -- 4 An Extended VPRS Model -- 5 Conclusion -- References -- Rough Set Applications -- Cross-Weighting Knowledge Distillation for Object Detection -- 1 Introduction -- 2 Related Work -- 2.1 Object Detection -- 2.2 Knowledge Distillation -- 3 Method -- 3.1 Cross-Weighting Knowledge Distillation -- 3.2 Valuable Distillation Object Selection Module -- 3.3 Overall Distillation Loss -- 4 Experiment -- 4.1 Experiment Settings -- 4.2 Implementation and Details -- 4.3 Main Results -- 4.4 Analysis -- 5 Conclusion -- References -- A Method of Multi-USV Reward Design Using Fuzzy Control -- 1 Introduction -- 2 Related Work -- 3 Preliminary -- 3.1 Reinforcement Learning -- 3.2 Fuzzy Control -- 3.3 Cloud Model -- 4 Proposed Method -- 4.1 Framework -- 4.2 Fuzzy Reward Design -- 5 Experiments -- 5.1 Simulation Environment -- 5.2 Comparison Analysis -- 6 Conclusion -- References -- Hyp-DAN: Hyperbolic Distance-Aware Attention Networks -- 1 Introduction -- 2 Related Works -- 3 Preliminaries -- 3.1 Hyperbolic Geometry Initiation and Application -- 3.2 Hyperbolic Operations -- 4 Networks -- 4.1 Hyperbolic Distance Attention -- 5 Experiments -- 6 Discussion and Conclusion -- References -- Optimizing Rough Set Flow Graph Inference -- 1 Introduction -- 2 Background -- 2.1 Bayesian Networks -- 2.2 Rough Set Flow Graphs -- 2.3 Marginalize All-at-once -- 2.4 Marginalize One-by-one. ; 3 Optimizing RSFG Inference.
• Titolo autorizzato: Rough Sets
• ISBN: 3-031-65665-2
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910878060803321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Computer Science Series