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[null,null,"Titolo",null,"Rough Sets : International Joint Conference, IJCRS 2023, Krakow, Poland, October 5-8, 2023, Proceedings / / Andrea Campagner [and five others], editors"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2023]"]
[{"$nulls":4},"©2023"]
[null,null,"ISBN",null,"3-031-50959-5"]
[null,null,"Edizione",null,"[First edition.]"]
[null,null,"Descrizione fisica",null,"1 online resource (686 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science Series ; ; Volume 14481"]
[null,null,"Disciplina",null,"511.32"]
[null,null,"Soggetti",null,"Rough sets"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Anniversary Talk -- Rough Sets in Interactive Granular Computing: Toward Foundations for Intelligent Systems Interacting with Human Experts and Complex Phenomena -- Keynote Lectures -- Rough-Calculus and Numerical Analysis - A Mathematical Foundation -- Can AI and Big Data Methods Really Help in Cyber Security? -- Applications of Tolerance-based Granular Methods (Extended Abstract) -- Role of Color in Histological Image Analysis: Rough-Fuzzy Computing to Deep Learning -- Big Data Intelligence: Challenges and Our Solutions -- Knowledge Engineering in Food Computing \\mdash Selected Problems and Applications (Extended Abstract) -- Model-agnostic Explanations of Black-box Prediction Models using Rough Sets - The Case of Post-Competition Analytics at KnowledgePit.ai (Keynote Abstract) -- Contents -- Rough Set Models -- Selected Approaches to Conflict Analysis Inspired by the Pawlak Model - Case Study -- 1 Introduction -- 2 Literature Review -- 3 Conflict Analysis Models - Case Study -- 3.1 Conflict Analysis Using the Pawlak Model and the Distance Function -- 3.2 Conflict Analysis Using the Pawlak Model and the Conflict Function -- 3.3 Conflict Analysis Using Hierarchical Clustering for Determining Disjoint Clusters -- 3.4 Conflict Analysis Using Negotiations Stage -- 3.5 Consensus Model -- 4 Comparison and Application Areas -- 5 Conclusions -- References -- Multi-heuristic Induction of Decision Rules -- 1 Introduction -- 2 Decision Rules -- 2.1 Heuristics for Induction of Decision Rules -- 2.2 Decision Rules Derived from Decision Trees -- 3 Global Optimization -- 4 Experimental Results -- 5 Conclusions -- "]
[{"$nulls":4},"References -- Algebraic Formulations and Geometric Interpretations of Decision-Theoretic Rough Sets -- 1 Introduction -- 2 Algebraic Formulations -- 2.1 Bayesian Decision Procedure -- 2.2 Two-Way Classification."]
[{"$nulls":4},"2.3 Three-Way Classification -- 3 Geometric Interpretations -- 3.1 Geometric Interpretation of the Bayesian Classification -- 3.2 Geometric Interpretation of Two-Way Classification -- 3.3 Geometric Interpretation of Three-Way Classification -- 4 Conclusions -- References -- Reduction of Binary Attributes: Rough Set Theory Versus Formal Concept Analysis -- 1 Introduction -- 2 Rudiments of Rough Sets and Formal Concept Analysis -- 2.1 Elements of Rough Set Theory -- 2.2 Elements of Formal Concept Analysis -- 3 Set Spaces - A Platform for Comparison of RST and FCA -- 4 Reduction of Binary Attributes in RST and Reduction of Attributes in FCA - A Comparison -- 5 Conclusions -- References -- An Acceleration Method for Attribute Reduction Based on Attribute Synthesis -- 1 Introduction -- 2 Preliminaries -- 2.1 Neighborhood Rough Set -- 2.2 Attribute Reduction -- 3 Reduction Based on Attribute Synthesis -- 4 Experimental Analysis -- 4.1 Time Consumption Comparison -- 4.2 Classification Accuracy Comparison -- 5 Conclusions and Future Perspectives -- References -- Attribute Reduction Based on the Multi-annulus Model -- 1 Introduction -- 2 Construction of Multi-annulus Model -- 3 Attribute Reduction Based on the Multi-annulus Model -- 4 Experiments -- 4.1 Datasets -- 4.2 Time Consumption Comparison -- 4.3 Classification Accuracy -- 4.4 Parameter Sensitivity -- 4.5 Conclusion -- References -- Foundations -- Deterministic and Nondeterministic Decision Trees for Decision Tables with Many-Valued Decisions from Closed Classes -- 1 Introduction -- 2 Main Definitions and Notation -- 2.1 Decision Tables -- 2.2 Deterministic and Nondeterministic Decision Trees -- 2.3 Complexity Measures -- 2.4 Parameters of Decision Trees and Tables -- 3 Main Results -- 3.1 Function F,A -- 3.2 Function G,A -- 3.3 Function H,A -- 3.4 Family of Closed Classes of Decision Tables."]
[{"$nulls":4},"3.5 Example of Information System -- 4 Conclusions -- References -- Paraconsistent Logics: A Survey Focussing on the Rough Set Approach -- 1 Introduction -- 2 Different Approaches to Paraconsistency -- 2.1 The 3-Valued Approach -- 2.2 Non-adjunctive Approach -- 2.3 Non-truth Functional Approach -- 2.4 Transformation Approach -- 2.5 Logics of Formal Inconsistency -- 2.6 Adaptive Approach -- 2.7 Relevance Approach -- 2.8 Rough Set Approach -- 3 Some New Paraconsistent Logics -- 3.1 The Systems Lri -- 3.2 Paraconsistency of Lr5 -- 4 Conclusions -- References -- Hexagons of Opposition in Linguistic Three-Way Decisions -- 1 Introduction -- 2 Preliminaries -- 2.1 Three-Way Decisions with Evaluative Linguistic Expressions -- 2.2 Hexagons of Opposition -- 3 Hexagons of Opposition in Linguistic Three-Way Decisions -- 3.1 Hexagons of Opposition with Evaluative Linguistic Expressions -- 3.2 Comparing Hexagon of Oppositions -- 3.3 Discussion on the Logical Relations Between Hexagons -- 4 Conclusions -- References -- Algebraic Models for Qualified Aggregation in General Rough Sets, and Reasoning Bias Discovery -- 1 Introduction -- 2 Background -- 2.1 T-Norms, S-Norms, Uninorms, and Implications -- 2.2 Implication Operations -- 3 Model of Rough Skeptic and Pessimistic Reasoning -- 3.1 Implications -- 3.2 Concrete and Abstract Algebraic Models -- 4 Illustrative Examples -- 4.1 Abstract Example -- 4.2 Detection of Reasoning and Algorithm Bias -- 5 Skeptical Aggregation and Rough Dependence -- 6 Directions -- References -- Two-Sorted Modal Logic for Formal and Rough Concepts -- 1 Introduction -- 1.1 Many-Sorted Polyadic Modal Logic -- 2 Two-"]
[{"$nulls":4},"Sorted Modal Logic and Concept Lattices -- 2.1 Two-Sorted Modal Logic and Concept Lattices in Rough Set Theory -- 2.2 Two Sorted Modal Logic and Concept Lattice in Formal Concept Analysis."]
[{"$nulls":4},"3 Logical Representation of Three Concept Lattices -- 4 Conclusion and Future Direction -- References -- Kryszkiewicz's Relation for Indiscernibility of Objects in Data Tables Containing Missing Values -- 1 Introduction -- 2 Pawlak's Approach -- 3 Lipski's Approach -- 3.1 Possible Tables and Possible Indiscernibility Relations -- 3.2 Possible Equivalence Classes -- 3.3 Aggregation of Possible Indiscernibility Relations -- 3.4 Lower and Upper Bounds of the Actual Approximations -- 4 Kryszkiewicz's Relation for Indiscernibility of Objects -- 5 Relationship Between Kryszkiewicz's Approach and Lipski's Approach -- 6 Conclusions -- References -- Algebraic, Topological, and Mereological Foundations of Existential Granules -- 1 Introduction -- 2 Background -- 2.1 Topological Vector Spaces -- 2.2 Partial Algebraic Systems -- 3 Existential Granular K-Means Algorithm -- 3.1 Partial Algebras for BKM Variants -- 4 The Granular Ball Methodologies -- 4.1 Fixing the Mathematics -- 5 Existential Granulations -- 5.1 Clean Rough Randomness and Models of Algorithms -- 5.2 Formalizing the BKM Algorithms -- 6 Further Directions -- References -- Aggregation Operators on Shadowed Sets Deriving from Conditional Events and Consensus Operators -- 1 Introduction -- 2 Preliminaries -- 2.1 Shadowed Sets -- 2.2 Aggregation Functions -- 3 How to Aggregate Shadowed Sets -- 3.1 Using Operations on Conditional Events -- 3.2 Using Orthopair-Like Operations -- 4 Aggregation Functions Generating Operations on Shadowed Sets -- 4.1 Aggregation Functions Generating Conditional-Event Conjunctions on Shadowed Sets -- 4.2 Aggregation Functions Generating Conditional-Event Disjunctions on Shadowed Sets -- 4.3 Aggregation Functions Generating *C -- 5 Conclusions and Future Works -- References -- Pawlak, Belnap and the Magical Number Seven -- 1 Introduction."]
[{"$nulls":4},"2 The Pawlak-Brouwer-Zadeh Distributive de Morgan Lattices and Indiscernibility-Based Rough Set Theory -- 2.1 Pawlak-Brouwer-Zadeh Lattices and Rough Set Theory -- 3 The Seven-Valued Logic of the Pawlak-Brouwer-Zadeh Lattice -- 4 Conclusions -- References -- Three-way Decisions -- Three-Way Conflict Analysis for Three-Valued Situation Tables with Rankings and Reference Tuples -- 1 Introduction -- 2 Preliminaries -- 3 Three-Way Conflict Analysis Models by Considering Rankings and Reference Tuples -- 3.1 Two Rankings of Issue Sets and Two Reference Tuples -- 3.2 Alliance and Conflict Measures with Weight Factors -- 4 An Example -- 5 Conclusion and Future Work -- References -- Three-Way Social Network Analysis: Triadic Measures at Three Levels -- 1 Introduction -- 2 A Tri-Level Framework of Triadic Measures -- 2.1 Triadic Measures of Centrality at the Node-Level -- 2.2 Triadic Measures of Importance at the Community-Level -- 2.3 Triadic Measures of Community-Importance at the Network-Level -- 3 Two Examples -- 4 Conclusion -- References -- Cognitive and Social Decision Making: Three-Way Decision Perspectives -- 1 Introduction -- 2 Three-Way Decision, Tri-Level Thinking, and SMV Spaces -- 2.1 An Overview of Three-Way Decision -- 2.2 Tri-Level Thinking and SMV Spaces -- 3 Three Perspectives on Cognitive and Social Decision Making -- 3.1 Research Framework Perspective -- 3.2 Social Hierarchy Perspective -- 3.3 Intelligence and Intelligent Systems Perspective -- 4 Concluding Remarks and Future Research Challenges -- References -- New Models of Three-Way Conflict Analysis for Incomplete Situation Tables -- 1 Introduction -- 2 Preliminaries -- 3 Three-Way Conflict Analysis Models for Incomplete Information Situation Tables -- 4 An Application "]
[{"$nulls":4},"of the Three-Way Conflict Analysis Model -- 5 Conclusion -- References -- Granular-Ball Three-Way Decision."]
[{"$nulls":4},"1 Introduction."]