Note: Intro -- Preface -- Organization -- Contents -- Rough Sets: The Experts Speak -- Decision-Oriented Rough Set Methods -- 1 Introduction -- 2 Decision-Oriented Rough Set Models and Methods -- 3 Concluding Remarks and Future Perspectives -- References -- On Generalized Decision Functions: Reducts, Networks and Ensembles -- 1 Introduction -- 2 Generalized Decision Functions -- 3 Simplified Conditional Independence -- 4 Generalized Decision Measures -- 5 Embedded Decision Reducts -- 6 Ensembles of Complementary Reducts -- 7 Attribute Decomposition Problem -- 8 Heuristics and Boolean Representation -- 9 Conclusions and Future Directions -- References -- Formalization of Medical Diagnostic Rules -- 1 Introduction -- 2 Background: Medical Diagnostic Process -- 2.1 RHINOS -- 2.2 Focusing Mechanism -- 3 Basics of Rule Definitions -- 3.1 Rough Sets -- 3.2 Classification Accuracy and Coverage -- 3.3 Probabilistic Rules -- 4 Formalization of Medical Diagnostic Rules -- 4.1 Deterministic Model -- 4.2 Probabilistic Model -- 5 New Rule Induction Model -- 6 Discussion: What Has Not Been Achieved? -- 7 Conclusion -- References -- Multi-granularity Intelligent Information Processing -- 1 Introduction -- 2 Multi-granularity Rough Set Theory -- 3 Multi-granularity Computing with Words -- 4 Multi-granularity Fuzzy Quotient Space Theory -- 5 Multi-granularity Cloud Model -- 6 Multi-granularity Clustering Based on Density Peaks -- 7 Conclusion -- References -- Granular Structures Induced by Interval Sets and Rough Sets -- 1 Introduction -- 2 Interval Sets -- 2.1 Interval Sets and Interval-Set Algebras -- 2.2 Inclusion Relations in Interval Sets -- 2.3 Granular Structure in Interval Sets -- 3 The Granular Structure Based on Order Relation in Interval Sets -- 3.1 Preference -- 3.2 Interval Set Comparisons. , 4 Granular Structures in Interval Sets from Set-Theoretic Perspectives -- 4.1 Granular Structure for (I(2U),w) -- 4.2 Granular Structure for (I(2U),w+) -- 4.3 Granular Structure for (I(2U),p) -- 4.4 Granular Structure for (I(2U),c) -- 5 Connections of Rough Sets and Interval Sets -- 6 Conclusions -- References -- Generalized Rough Sets -- Empirical Risk Minimization for Variable Consistency Dominance-Based Rough Set Approach -- 1 Introduction -- 2 Variable Consistency Dominance-Based Rough Set Approach -- 3 Empirical Risk Minimization -- 4 Concluding Remarks -- References -- Rough Set Approximations in Multi-scale Interval Information Systems -- 1 Introduction -- 2 Interval Information Systems -- 2.1 Information Systems -- 2.2 Interval Information Systems -- 3 Multi-scale Interval Information Systems -- 3.1 Multi-scale Information Systems -- 3.2 Multi-scale Interval Information Systems -- 4 Rough Set Approximations -- 5 Conclusion -- References -- A New Subsystem-Based Definition of Generalized Rough Set Model -- 1 Introduction -- 2 Preliminaries -- 3 A New Subsystem-Based Definition of Generalized Rough Set Model -- 4 Conclusion -- References -- A Comparison of Two Types of Covering-Based Rough Sets Through the Complement of Coverings -- 1 Introduction -- 2 Preliminaries -- 3 Relationships Between FL, FH and SL, SH Through the Complement of Coverings -- 3.1 Complementary Neighborhood -- 3.2 The Complement of a Covering and Relationships between the Two Types of Covering-Based Rough Sets -- 3.3 Conditions Under Which FH and SH Are Identical -- 4 Extension of a Covering -- 5 Matroidal Approach and the Exact Sets -- 6 Conclusions -- References -- On the Nearness Measures of Near Sets -- 1 Introduction -- 2 Preliminaries -- 3 Nearness Measures -- 4 Strong Nearness Relations -- 5 Concluding Remarks -- References. , Topological Properties for Approximation Operators in Covering Based Rough Sets -- 1 Introduction -- 2 Preliminaries -- 2.1 Pawlak's Rough Set Approximations -- 2.2 Closures -- 2.3 Covering Based Rough Sets -- 2.4 Other Framework of Lower and Upper Approximations -- 2.5 New Framework of Approximation Operators -- 3 Topological Characterization of Upper Approximations -- 4 Algebraic and Topological Properties -- 5 Conclusions -- References -- Rough Sets and Graphs -- Preclusivity and Simple Graphs -- 1 Introduction -- 2 Preliminary Notions -- 2.1 Graphs -- 2.2 Preclusivity Spaces -- 2.3 Formal Concept Analysis -- 3 Simple Graphs as Preclusivity Spaces -- 3.1 Two Basic Cases -- 4 The Cube of Opposition Generated by the Preclusive Relation -- 5 Conclusion -- References -- Preclusivity and Simple Graphs: The n--cycle and n--path Cases -- 1 Introduction -- 2 Preliminary Notions -- 2.1 Graphs -- 2.2 Preclusivity Spaces -- 2.3 Simple Graphs as Preclusivity Spaces -- 3 The Case of Cn -- 4 The Case of Pn -- 5 Conclusion -- References -- Connectedness of Graph and Matroid by Covering-Based Rough Sets -- 1 Introduction -- 2 Basic Definitions -- 2.1 Rough Set -- 2.2 Matroid -- 2.3 Graph -- 3 Covering Induced by Graph -- 4 The Connectedness of Matroid Induced by Covering -- 5 Conclusions -- References -- Controllability in Directed Complex Networks: Granular Computing Perspective -- 1 Introduction -- 2 Definitions -- 2.1 Controllability of Complex Networks Based on the Linear System -- 2.2 Granular Computing -- 3 The Techniques to Enhance Controllability of Directed Complex Networks Based on GrC -- 4 Examples -- 5 Conclusions and Future Work -- References -- Rough and Fuzzy Hybridization -- Dynamic Maintenance of Rough Fuzzy Approximations with the Variation of Objects and Attributes -- 1 Introduction -- 2 Preliminaries. , 3 Matrix Representation of the Lower and Upper Approximations in the FDS -- 4 Dynamically Maintenance of Approximations in the FDS Under the Variation of Attributes and Objects -- 5 An Illustrative Example -- 6 Conclusions -- References -- Semi-Supervised Fuzzy-Rough Feature Selection -- 1 Introduction -- 2 Rough and Fuzzy-Rough Set Theory -- 3 Semi-Supervised Fuzzy-Rough Feature Selection -- 4 Experimental Evaluation -- 4.1 Experimental Setup -- 4.2 Results -- 5 Conclusion -- References -- Modified Generalised Fuzzy Petri Nets for Rule-Based Systems -- 1 Introduction -- 2 Preliminaries -- 2.1 Triangular Norms -- 2.2 Fuzzy Implications -- 3 Generalised Fuzzy Petri Nets -- 4 Modified Generalised Fuzzy Petri Nets -- 5 Example -- 6 Concluding Remarks -- References -- Fuzzy Rough Decision Trees for Multi-label Classification -- 1 Introduction -- 2 Related Works -- 2.1 Multi-label Decision Trees -- 2.2 Multi-label Feature Evaluation with Fuzzy Rough Sets -- 3 Multi-label Fuzzy Rough Decision Trees -- 4 Experiments -- 4.1 A Toy Example -- 4.2 Numerical Experiments -- 4.3 Experiment Results -- 5 Conclusions -- References -- Axiomatic Characterizations of Reflexive and T-Transitive I-Intuitionistic Fuzzy Rough Approximation Operators -- 1 Introduction -- 2 Preliminaries -- 2.1 Intuitionistic Fuzzy Logical Operators -- 2.2 Intuitionistic Fuzzy Sets -- 3 Constructive Definitions of I-Intuitionistic Fuzzy Rough Approximation Operators -- 4 Axioms of I-Intuitionistic Fuzzy Rough Approximation Operators -- 5 Conclusion -- References -- Granular Computing -- Knowledge Supported Refinements for Rough Granular Computing: A Case of Life Insurance Industry -- Abstract -- 1 Introduction -- 2 Preliminary -- 2.1 Rough Set Approach (RSA) and Extended Applications -- 2.2 DEMATEL-Based ANP (DANP) Method -- 3 Research Approach. , 3.1 Dominance-Based Rough Set Approach (DRSA) -- 3.2 DEMATEL-Based ANP Method (DANP) -- 3.3 Suggested Steps for the Proposed Approach -- 4 Empirical Case of Life Insurance Industry in Taiwan -- 4.1 Data -- 4.2 Knowledge-Supported Refinements of Approximation Spaces -- 5 Conclusion and Remarks -- References -- Building Granular Systems - from Concepts to Applications -- 1 Introduction -- 2 Information Granulation -- 3 Models of Granules -- 4 Models of Computing with Granules -- 5 Evaluation of Granular Systems -- 6 Conclusions -- References -- The Rough Granular Approach to Classifier Synthesis by Means of SVM -- 1 Introduction -- 1.1 Motivation -- 1.2 Methodology -- 1.3 Granulation in Rough Mereology -- 1.4 Support Vector Machine Classifier -- 2 Optimized Concept Dependent -Granulation -- 3 Experimental Session -- 3.1 Results of Experiments -- 4 Conclusions -- References -- Data Mining and Machine Learning -- The Boosting and Bootstrap Ensemble for Classifiers Based on Weak Rough Inclusions -- 1 Introduction -- 1.1 Theoretical Background of Our Classifiers -- 2 8_v1.1-8_v1.5 Classifiers -- 3 Classifiers Stabilisation Methods -- 3.1 Bootstrap Ensembles -- 3.2 Boosting Based on Arcing -- 3.3 Boosting Based on Ada-Boost with Monte Carlo Split -- 4 Experimental Session -- 4.1 The Results of Experiments -- 5 Conclusions -- References -- Extraction of Off-Line Handwritten Characters Based on a Soft K-Segments for Principal Curves -- 1 Introduction -- 2 A Soft K-segments Algorithm for Principal Curves -- 2.1 Principal Curves -- 2.2 A Soft K-segments Algorithm for Principal Curves -- 3 Structural Extraction of Off-Line Handwritten Characters Based on a Soft K-segments Principal Curves -- 4 Conclusions -- References -- A Knowledge Acquisition Model Based on Formal Concept Analysis in Complex Information Systems -- 1 Introduction -- 2 Basic Notions of FCA. , 3 Classification Analysis in Domain of Attribute Based On GrC.