Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing

Name: Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing | 15th International Conference, RSFDGrC 2015, Tianjin, China, November 20-23, 2015, Proceedings
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15th International Conference, RSFDGrC 2015, Tianjin, China, November 20-23, 2015, Proceedings

Yiyu Yao Qinghua Hu Hong Yu Jerzy W. Grzymala-Busse(Herausgeber*in)

Springer (Verlag)

Erschienen am 21. November 2015

XVI, 502 Seiten

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978-3-319-25783-9 (ISBN)

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Inhalt

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
4 One-Valued Formal Contexts
5 Algebraic Structure in the Complex Information System
6 Knowledge Acquisition in Complex Information Systems
7 Conclusions
References
The Borda Count, the Intersection and the Highest Rank Method in a Dispersed Decision-Making System
1 Introduction
2 A Brief Overview of Decision-Making System Using Dispersed Knowledge
3 Methods of Conflict Analysis
4 Experiments
5 Conclusions
References
An Ensemble Learning Approach Based on Missing-Valued Tables
1 Introduction
2 Preliminaries
2.1 Data Mining Based on Rough Sets
2.2 Missing Value Handling in Rough Sets
2.3 Ensemble Learning
3 An Ensemble Learning Approach Based on Missing-Valued Decision Tables
4 Numerical Experiments
4.1 Classification Error Rates by Missing Value Rate
4.2 Comparison with Other Methods
5 Effects on the Robustness Against Attributes Deficiencies
6 Concluding Remarks
References
Multiple-Side Multiple-Learner for Incomplete Data Classification
1 Introduction
2 Related Work
3 MSML
3.1 Chi-Square Statistic Feature Evaluation Algorithm
3.2 Multiple-Side Multiple-Learner for Incomplete Data
4 Experiments
4.1 Experimental Description
4.2 Experimental Results and Analysis
5 Conclusion and Discussion
References
Sparse Matrix Feature Selection in Multi-label Learning
1 Introduction
2 Related Works
2.1 Multi-Label K-Nearest-Neighbor
2.2 Feature Selection with ReliefF
3 Evaluation Metrics
4 Feature Selection Base on Sparse Representation
4.1 Feature Selection via Sparse Representation
4.2 Algorithm for Feature Selection
5 Experiments
5.1 Dataset
5.2 Experimental Setting
5.3 Experimental Result and Analysis
6 Conclusions and Further Studies
References
A Classification Method for Imbalanced Data Based on SMOTE and Fuzzy Rough Nearest Neighbor Algorithm
1 Introduction
2 Background
2.1 Imbalanced Data Problem
2.2 Existing Solutions of Imbalanced Data Problem
3 The Proposed Method: A Combination of DB_SMOTE and FRNN
3.1 DB_SMOTE (Distance-based Synthetic Minority Over-Sampling Technique)
3.2 FRNN (Fuzzy Rough Nearest Neighbor)
3.3 The Algorithm of DB_SMOTE + FRNN
4 Experiments and Analysis
4.1 Evaluation
4.2 Description of Data Sets
4.3 Experiments Results
5 Conclusion
References
Three-Way Decisions
Region Vector Based Attribute Reducts in Decision-Theoretic Rough Sets
1 Introduction
2 Three-Way Approximations of a Classification
3 Region Vector Based Attribute Reducts for DTRS Models
3.1 An Analysis of Set Based Decision Regions
3.2 Region Vector Based Attribute Reducts
4 A Reduct Construction Method
5 Conclusion
References
How to Evaluate Three-Way Decisions Based Binary Classification?
1 Introduction
2 Three-Way Decisions Based Classification
3 Performance Measures for Two-Way Decisions Based Binary Classification
4 Performance Measures for Three-Way Decisions Based Classification
4.1 Numerical Measures
4.2 Graphical Measures
4.3 Remarks
5 Conclusions
References
A Moderate Attribute Reduction Approach in Decision-Theoretic Rough Set
1 Introduction
2 Preliminary Knowledge of Yao's DTRS
3 Attribute Reduction in DTRS
3.1 Lower Approximation Monotonicity Based Attribute Reduction (LAMAR)
3.2 Cost Minimum Based Attribute Reduction(CMAR)
3.3 Lower-Approximation-Monotonicity Fusion with Cost Minor Attribute Reduction(LAMFCMAR)
3.4 Genetic Algorithm for Attrition Reduction
4 Experimental Analysis
5 Conclusion
References
Decisions Tree Learning Method Based on Three-Way Decisions
1 Introduction
2 Preliminaries
2.1 ID3 Decision Tree Learning Algorithm
2.2 The Three-Way Decision
3 The Construction of Three-Way Decision Tree
3.1 The Construction of Conditional Probability in the Three-Way Decision
3.2 The Construction Algorithm of the Three-Way Decision Tree
4 The Merger and Pruning Rules of the Three-Way Decision Tree
5 Example Verification
6 Conclusions and Future Work
References
Multi-decision-makers-based Monotonic Variable Consistency Rough Set Approach with Multiple Attributes and Criteria
1 Introduction
2 Analysis of Decision Expression System from Views of the Pansystems Theory
3 Granules of Knowledge Referred to the Relation System and Decision Classes Referred to the Decision-Making System
4 Monotonic Variable Consistency Measures Referred to the Causal System
5 Multi-decision-makers-based Monotonic Variable Consistency Rough Set Approach with Multiple Attributes and Criteria
6 Conclusion
References
Determining Three-Way Decision Regions by Combining Gini Objective Functions and GTRS
1 Introduction
2 Background Knowledge
2.1 Three-Way Decisions
2.2 Concepts in Game Theory
3 Solving Gini Objective Functions with GTRS
3.1 Gini Objective Function
3.2 Game Formulation and Analysis
4 An Example
5 Conclusion
References
IJCRS 2015 Data Challenge
Mining Data from Coal Mines: IJCRS'15 Data Challenge
1 Introduction
2 Knowledge Pit Data Challenge Platform
3 Scope of the IJCRS'15 Data Challenge
3.1 The Competition Data
3.2 Evaluation of Submissions
4 Results of the Competition
5 Summary and the Future Research
References
Prediction of Methane Outbreak in Coal Mines from Historical Sensor Data under Distribution Drift
1 Introduction
2 Supervised Classification Framework
2.1 Optimal Discretization
2.2 Bayesian Approach for Variable Selection
2.3 Compression-Based Model Averaging
2.4 Automatic Variable Construction for Multi-Table
3 Applying the Framework for the Challenge
3.1 Choice of the Classification Framework
3.2 Application to the Challenge Dataset
4 Challenge Submissions
4.1 Preliminary Trials
4.2 A Methodology to Reduce the Drift Problem
4.3 Simplification of the Solution
4.4 Further Improvement
4.5 Insights on Relevant Variables
5 Conclusion
References
Window-Based Feature Engineering for Prediction of Methane Threats in Coal Mines
1 Introduction
2 The Data Set and the Problem
3 Evaluation of Results
4 Window-Based Feature Extraction
5 Feature Selection and the Course of Experiments
6 Results and Remarks
7 DISESOR
8 Summary
References
SVM Parameter Tuning with Grid Search and Its Impact on Reduction of Model Over-fitting
1 Introduction
2 Challenge Description
3 Building Classification Models with SVMs
4 SVM Parameter Tuning with Grid Search
5 Feature Modeling and Selection
5.1 Histogram-Based Modeling of Time Series Data
5.2 Using Time Series of the Target Sensors
5.3 Using Samples from the Time Series of the Target Sensors
5.4 Using Time Series of the Target Sensors and Their Predecessors
6 Results
7 Conclusion
References
Detecting Methane Outbreaks from Time Series Data with Deep Neural Networks
1 Introduction
2 Problem Statement
2.1 Data
2.2 Evaluation
2.3 Challenges
3 Long Short-Term Memory Model
3.1 Overview
3.2 Data Preprocessing
3.3 Network Architecture and Training
4 Final Ensemble Model
4.1 Ensembling Methods
4.2 Deep Feedforward Neural Network as a Base Learner
4.3 Ensembling in Our Solution
4.4 Results and Discussion
5 Conclusion
References
Self-Organized Predictor of Methane Concentration Warnings in Coal Mines
1 Introduction
1.1 Problem Formulation
1.2 Data Summarization and Filtering
2 Hierarchical Greedy Model Composition
2.1 Diversified Backward-Forward Feature Selection
2.2 Predictive Model Selection
3 Experimental Results
4 Evaluation and Conlcusions
References
Prediction of Methane Outbreaks in Coal Mines from Multivariate Time Series Using Random Forest
1 Introduction
2 The Competition Task
2.1 Data
2.2 Evaluation
3 Solution Overview
3.1 Derived Time Series
4 Feature Engineering
4.1 Generated Features
4.2 Correlations
5 Classification
6 Conclusions
References
Correction to: Building Granular Systems - from Concepts to Applications
Correction to: Chapter "Building Granular Systems - from Concepts to Applications" in Y. Yao et al. (Eds.): Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, LNAI 9437, https://doi.org/10.1007/978-3-319-25783-9_22
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