Rough Sets and Knowledge Technology

Name: Rough Sets and Knowledge Technology | 10th International Conference, RSKT 2015, Held as Part of the International Joint Conference on Rough Sets, IJCRS 2015, Tianjin, China, November 20-23, 2015, Proceedings
Brand: Springer
Price: 53.49 EUR
Availability: OnlineOnly

10th International Conference, RSKT 2015, Held as Part of the International Joint Conference on Rough Sets, IJCRS 2015, Tianjin, China, November 20-23, 2015, Proceedings

Davide Ciucci Guoyin Wang Sushmita Mitra Wei-Zhi Wu(Herausgeber*in)

Springer (Verlag)

Erschienen am 21. November 2015

XVI, 538 Seiten

E-Book

PDF mit Wasserzeichen-DRM

Systemvoraussetzungen

978-3-319-25754-9 (ISBN)

53,49 €inkl. 7% MwSt.

Systemvoraussetzungen

für PDF mit Wasserzeichen-DRM

E-Book Einzellizenz

Als Download verfügbar

Beschreibung

Weitere Details

Weitere Ausgaben

Inhalt

Intro
Preface
Organization
Contents
Rough Sets: The Experts Speak
A Rough Set Approach to Incomplete Data
1 Introduction
2 Fundamental Concepts
3 Lower and Upper Approximations
4 Probabilistic Approximations
5 Local Approximations
6 Special Topics
7 Conclusions
References
PICKT: A Solution for Big Data Analysis
1 Introduction
2 Parallel Computing for the Volume
3 Incremental Learning for the Velocity
4 Composite Rough Set Model for the Variety
5 Knowledge Discovery for the Value
6 Three-Way Decisions for the Veracity
7 Conclusion
References
A Further Investigation to Relative Reducts of Decision Information Systems
1 Introduction
2 Preliminaries
3 Relative Reducts
4 Discernibility Matrix and Discernibility Function
4.1 Classical Discernibility Matrix and Discernibility Function
4.2 Elements-Based Discernibility Matrixes and Discernibility Functions
4.3 Blocks-Based Discernibility Matrixes and Discernibility Functions
5 Conclusions
References
Rough Sets - Past, Present and Future: Some Notes
1 Rough Set Theory: A Brilliant Past Facing the Future
2 Multi-agent Approximation Spaces
3 The Mathematics of Rough Sets
4 The Relational Approach to Rough Sets
5 Rough Set Theory and Logic
6 Additional Topics to Be Investigated
References
Interactive Granular Computing
1 Introduction
2 Complex Granules
3 Adaptive Judgement
4 Conclusions
References
Rough Sets and Three-Way Decisions
1 Introduction
2 Two Representations of Rough Set Approximations
3 Refining Formulations of Three-Way Decisions
3.1 Probabilistic Three-Way Classifications
3.2 Evaluation-Based Three-Way Decisions
3.3 A Trisecting-and-Acting Framework of Three-Way Decisions
4 Future Research in Three-Way Decisions
5 Conclusion
References
Tutorial
Rough Set Tools for Practical Data Exploration
1 Introduction
2 Evolution of Rough Set Software for Data Analysis
3 RapidMiner Predictive Analytics Platform
4 RapidMiner Extensions
5 RoughSets Package for R System
6 RapidRoughSets Extension for RapidMiner
7 Summary and the Road Ahead
References
Reducts and Rules
Dominance-Based Neighborhood Rough Sets and Its Attribute Reduction
1 Introduction
2 Preliminaries
2.1 Dominance-Based Rough Set Approach
2.2 Neighborhood Rough Set
3 Dominance-Based Neighborhood Rough Set
3.1 Hybrid Decision System
3.2 Normalization in HDS
3.3 Approximations in DNRS
4 Attribute Reduction in DNRS
4.1 Discernibility Matrix in DNRS
4.2 Minimal Discernibility Attribute Set Based Reduction in DNRS
4.3 Matrix of the Attribute Importance
4.4 Generation of the Reduct
5 Conclusion
References
Mining Incomplete Data with Many Lost and Attribute-Concept Values
1 Introduction
2 Data Sets
3 Probabilistic Approximations
4 Experiments
5 Conclusions
References
Inconsistent Dominance Principle Based Attribute Reduction in Ordered Information Systems
1 Introuction
2 Preliminaries
3 Inconsistent Dominance Principle Reduction
3.1 Theories of Reduction Based Inconsistent Dominance Principle
3.2 Approach to Reduction Based Inconsistent Dominance Principle
4 Conclusion
References
Improving Indiscernibility Matrix Based Approach for Attribute Reduction
1 Introduction
2 Indiscernibility Matrix Based Attribute Reduction
3 Indiscernibility Matrix Based Algorithms for Attribute Reduction
4 Experiments
5 Conclusions
References
Imprecise Rules for Data Privacy
1 Introduction
2 Rough Set Approach to Classification
3 Imprecise Rules and k-Anonymity
4 The Proposed Method for Inducing k-Anonymous Rules
5 Numerical Experiments
5.1 Data-Sets and Cross Validation
5.2 Comparison with the Conventional Classifier with Precise Rules
5.3 Comparison with the Classifier with k-Anonymous Precise Rules
6 Concluding Remarks
References
Proposal for a Statistical Reduct Method for Decision Tables
1 Introduction
2 Conventional Rough Sets and Reduct Method
3 Retests of the Conventional Reduct Method
4 Proposal of Statistical Reduct
4.1 Statistical Global Reduct Method
4.2 Statistical Local Reduct Method
5 An Example Application on an Open Dataset
6 Conclusions
References
Computation of Cores in Big Datasets: An FPGA Approach
1 Introduction
2 Introductory Information
2.1 Cores in the Rough Set Theory
2.2 Algorithm CORE-DDM for Generating Core Using Discernibility Matrix
2.3 Basic Notions for CORE-CT Algorithm
2.4 Algorithm CORE-CT for Generating Core Using Counting Table
2.5 Data to Conduct Experimental Research
3 Hardware Implementation
4 Experimental Results
5 Conclusions and Future Research
References
Knowledge Spaces and Reduction of Covering Approximation Spaces
1 Introduction
2 Preliminaries
2.1 Knowledge Spaces and Interior Operators
2.2 Covering Rough Approximations
3 Knowledge Spaces of Covering Approximation Spaces
4 Independence and Reduction of Covering Approximation Spaces
5 Conclusions
References
Families of the Granules for Association Rules and Their Properties
1 Introduction
2 A Family of the Granules in DIS
2.1 Preliminary
2.2 A Family of the Granules Defined by an Implication
3 A Family of the Granules in NIS
3.1 Preliminary
3.2 A Family of the Granules in NIS
3.3 Criterion Values of an Implication in NIS
4 Criterion Values and Apriori-Based Rule Generation
4.1 Current Rule Generation by Criteria support and accuracy
4.2 Rule Generation by Criteria support, accuracy, and coverage
5 Concluding Remarks
References
Generalized Rough Sets
New Neighborhood Based Rough Sets
1 Introduction
2 Preliminaries
3 Neighborhood Operators
3.1 Inverse Neighborhood Operators
3.2 Partial Order Relation for Neighborhood Operators
4 Hasse Diagram of Approximation Operators
4.1 Element Based Approximation Operators
4.2 Partial Order Relations of Other Approximation Operators
5 Conclusion and Future Work
References
Rough Sets and Textural Neighbourhoods
1 Introduction
2 Textures
3 Direlations
4 Textural Neighbourhoods
5 Singleton Approximations
6 Subset Approximations
7 Definability
8 Relation Preserving Functions
9 Conclusion
References
Matrix Approaches for Variable Precision Rough Approximations
1 Introduction
2 Preliminaries
3 -approximations and Strong -approximations
4 Calculation for Approximations of VPRS
5 Conclusions
References
The Lower Approximation Number in Covering-Based Rough Set
1 Introduction
2 Preliminaries
3 The Lower Approximation Number
3.1 Properties of the Lower Approximation Number
3.2 Lattice Establish with the Lower Approximation Number
3.3 A Pair of Matroid Approximation Operators
4 Conclusion
References
The Matroidal Structures of the Second Type of Covering-Based Rough Set
1 Introduction
2 Basic Definitions
2.1 Closure Systems and Closure Operators
2.2 The Second Type of Covering-Based Rough Sets
2.3 Matroids
3 Matroidal Structure of the Second Type of Covering Lower Approximation Operator
4 Matroidal Structure of the Second Type of Covering Upper Approximation Operator
5 Conclusions
References
Incremental Updating Rough Approximations in Interval-valued Information Systems
1 Introduction
2 Preliminaries
3 Incremental Approaches for Updating Rough Approximations on the Variation of Attributes
3.1 Adding a New Attribute Set
3.2 Deleting an Attribute Set
4 A Comparative Illustration
5 Conclusion
References
Three-Way Decision
Methods and Practices of Three-Way Decisions for Complex Problem Solving
1 Introduction
2 Studies on the Basic Issues of Three-Way Decisions
3 Three-Way Decisions with Rough Sets
4 Three-Way Decisions with Other Theories
5 Applications of Three-Way Decisions
6 Conclusions
References
A Multi-view Decision Model Based on CCA
1 Introduction
2 Related Work
2.1 An Overview of Three-Way Decisions Model Based on CCA
2.2 Multi-granular Mining for Boundary Region
3 Multi-view Decision Model Based on Constructive Three-Way Decision Theory
4 Experiments
4.1 Experimental Settings
4.2 Experimental Results
5 Conclusion
References
A Teacher-Cost-Sensitive Decision-Theoretic Rough Set Model
1 Introduction
2 Three Types of Costs
2.1 Misclassification Cost
2.2 Delay Cost
2.3 Teacher Cost
3 Teacher-Cost-Sensitive DTRS Model
4 Comparisons of the Three Models
4.1 An Example
4.2 Micro-Level Analysis
4.3 Macro-Level Analysis
5 Conclusions
References
A Three-Way Decision Making Approach to Malware Analysis
1 Introduction
2 Malware Analysis
3 Three-Way Decisions with Probabilistic Rough Sets
3.1 Game-Theoretic Rough Sets
3.2 Information-Theoretic Rough Sets
4 A Three-Way Approach for Malware Analysis
5 A Demonstrative Example
5.1 Three-Way Decisions Based on Game-Theoretic Rough Sets
5.2 Three-Way Decisions Based on Information-Theoretic Rough Sets
6 Conclusion
References
Chinese Emotion Recognition Based on Three-Way Decisions
Abstract
1 Introduction
2 Related Work
2.1 Latent Dirichlet Model
2.2 Three-Way Decisions Theory
3 Chinese Emotion Recognition
3.1 Emotion Dictionary
3.2 Emotion Vector Space Model
3.3 Emotion Vector Space Model Based on Topics
3.4 Chinese Emotion Recognition Based on Three-Way Decisions
3.5 Multi-label Emotion Recognition Framework
4 Experiment and Analysis
4.1 Experiment Data
4.2 Experiment Setting
4.3 Analysis of Experiments Result
5 Conclusion
References
Statistical Interpretations of Three-Way Decisions
1 Introduction
2 Overview of Evaluation Based Three-Way Decisions
3 A Statistical Framework for Interpreting Three-Way Decisions
3.1 General Considerations
3.2 Interpretations Through Median and Percentile
3.3 Interpretations Through Mean and Standard Deviation
4 Conclusion
References
Decision-Level Sensor-Fusion Based on DTRS
1 Introduction
2 Sensor Fusion Techniques
3 Rough Set Theory
4 A Cost-Sensitive Three-Way Decision Approach
4.1 The General Form
4.2 Computing Thresholds Based on DTRS
5 An Example
6 Conclusions and Future Work
References
Logic and Algebra
Antichain Based Semantics for Rough Sets
1 Introduction
2 Anti Chains for Representation
3 Quasi Equivalential Rough Set Theory
4 Semantics of QE-Rough Sets
5 Aggregations of AC Semantics
6 Further Directions and Remarks
References
Formalizing Lattice-Theoretical Aspects of Rough and Fuzzy Sets
1 Introduction
2 Lattice Theory -- Formally and Informally
3 Incomplete Information and Set Theory
4 Rough Sets and Approximation Spaces
5 From Lattices of Fuzzy Sets to L-Fuzzy Sets
6 Comparison of Both Developments
7 Conclusions and Future Work
References
Generalized Fuzzy Regular Filters on Residuated Lattices
1 Introduction
2 Basic Results on Residuated Lattices
3 Main Results
3.1 (, qk)-Fuzzy Positive Implicative (G-) Filter
3.2 (, qk)-Fuzzy MV (Fantastic) Filters
3.3 (, Qk)-Fuzzy Regular Filters
4 Conclusions
References
Approximations on Normal Forms in Rough-Fuzzy Predicate Calculus
1 Introduction
2 Mathematical Aspects
2.1 Rough-Fuzzy Connectives
3 Rough-Fuzzy Normal Forms
3.1 Disjunctive Normal Forms (DNF)
3.2 Conjunctive Normal Forms (CNF)
3.3 Principal Disjunctive Normal Forms (PDNF)
3.4 Principal Conjunctive Normal Forms (PCNF)
4 Conclusion
References
Clustering
Combining Rough Clustering Schemes as a Rough Ensemble
1 Introduction
2 Rough Set and Clustering
3 Ensemble Clustering
4 Study Data and Initial Rough Clustering
4.1 Dataset and Knowledge Representation
4.2 Individual Ordered Rough Clustering Using Two Knowledge Representations
5 Ensemble of Two Rough Ordered Clustering Scheme
5.1 Algorithm: Rough Ensemble Clustering
6 Discussion and Analysis of the Resulting Rough Cluster Ensemble
6.1 Initial Rough Clustering Using Percentile Values and Black Scholes Index
6.2 Comparison with Conventional Clustering Ensemble Techniques
7 Conclusion
References
Water Quality Prediction Based on a Novel Fuzzy Time Series Model and Automatic Clustering Techniques
1 Introduction
2 Preliminaries
3 An Automatic Clustering Algorithm
4 A Novel Method for Water Quality Prediction Based on Fuzzy Time Series Model and Automatic Clustering Techniques
5 Experimental Results
5.1 Water Temperature Forecasting
5.2 The Potential of Hydrogen Forecasting
6 Conclusions
References
Clustering Algorithm Based on Fruit Fly Optimization
Abstract
1 Introduction
2 Fruit Fly Optimization Algorithm
3 FOCA Approach
3.1 Motivation of FOCA
3.2 Implementation of FOCA
3.2.1 The Optimum Range Value of Initial Position
3.2.2 The Optimum Range Value of Step Size
3.2.3 Shock Factor
3.2.4 Fitness Function
4 Experimental Study
4.1 Datasets
4.2 Results
5 Conclusion
Acknowledgements
References
Rough Sets and Graphs
Rough Set Theory Applied to Simple Undirected Graphs
1 Introduction
2 Basic Notions
2.1 Rough Set Notation
2.2 Graphs
3 The Adjacency Matrix as an Information Table
4 Rough Approximations and Dependency of Graphs
4.1 Lower and Upper Approximations
4.2 Rough Membership and Dependency
5 Discernibility Matrix in Graph Theory
5.1 Cases of Kn and Kp,q
5.2 Case of Cn
5.3 Case of Pn
6 Conclusion
References
The Connectivity of the Covering Approximation Space
1 Introduction
2 Preliminaries
2.1 Covering Approximation Space
2.2 Graph Theory
3 Maximization of a Covering
4 The Connectivity of Covering Approximation Space
5 Methods of Judging the Connectivity of a Covering Approximation Space
5.1 From the Matrix Perspective
5.2 From the Graph Perspective
5.3 From Covering Perspective
6 Conclusions
References
Detecting Overlapping Communities with Triangle-Based Rough Local Expansion Method
1 Introduction
2 Preliminaries
3 Triangle-Based Rough Local Expansion Method
3.1 Community Detection Method Based on Rough Neighborhood
3.2 Neighborhood Expansion Strategy with Triangle Optimization
3.3 The Heuristic Algorithm Based on Triangle Optimization
3.4 The Heuristic Local Expansion Algorithm
4 Experiments and Analysis
5 Conclusions
References
Modeling and Learning
Rough Sets for Finite Mixture Model Based HEp-2 Cell Segmentation
1 Introduction
2 Stomped Normal Distribution
3 SNFM: Proposed Segmentation Algorithm
4 Experimental Results and Discussion
4.1 Importance of the Width of SN Distribution
4.2 Performance of Proposed SNFM Algorithm
4.3 Performance of Different Clustering Algorithms
5 Conclusion
References
Water Quality Prediction Based on an Improved ARIMA- RBF Model Facilitated by Remote Sensing Applications
Abstract
1 Introduction
2 Materials and Methods
2.1 Study Area
2.2 Data Sources
2.3 Digital Data Processing
2.4 Fundamental Theory
3 Experiments and Analysis
3.1 Calibration of Models Relating HJ-1 and Total Nitrogen Data
3.2 Forecasting of Water Quality
4 Conclusions
Acknowledgements
References
Roughness in Timed Transition Systems Modeling Propagation of Plasmodium
1 Introduction
2 Basic Definitions
2.1 Rough Sets
2.2 Transition Systems
3 Roughness in Timed Transition Systems
4 Modeling Propagation of Plasmodium
5 Conclusions
References
A Study on Similarity Calculation Method for API Invocation Sequences
1 Introduction
2 Related Work
3 The Similarity Measurement System
3.1 Overall System Architecture
3.2 Extraction of API Calling Sequence and Transformation to API Code Sequence
3.3 Similarity Calculation of API Code Sequences
4 Experiment
4.1 Similarity Results Among the Malware in Different or Same Family
4.2 Similarity Results Between Malware and Benign Programs
4.3 Measurement of Time Performance
5 Discussion
References
Fast Human Detection Using Deformable Part Model at the Selected Candidate Detection Positions
1 Introduction
2 Related Work
2.1 Deformable Part Model
2.2 Drawbacks and Corresponding Reasons of the DPM
2.3 EdgeBoxes and BING
3 DPM at Selected Candidate Detection Positions
4 Experimental Results
5 Conclusion
References
A New Method for Driver Fatigue Detection Based on Eye State
1 Introduction
2 System Design
3 Fatigue Detection Algorithm Design
3.1 Face Detection
3.2 Extraction the Eye Area
3.3 Eye Detection
3.4 Eye State Analysis
4 Experimental Results
5 Conclusion
References
Facial Expression Recognition Based on Quaternion-Space and Multi-features Fusion
1 Introduction
2 Introduction of Quaternion Theory
3 Facial Expression Recognition Method Based on Multi-features Fusion
3.1 Framework of the Proposed Method
3.2 PCA+CCA Framework
3.3 Quaternion-Space Combinative Feature
3.4 Quaternion-Space HDA Method
4 Experiments
4.1 Experimental Settings
4.2 Experimental Results Analysis
5 Conclusion
References
Correction to: Interactive Granular Computing
Correction to: Chapter "Interactive Granular Computing" in: D. Ciucci et al. (Eds.): Rough Sets and Knowledge Technology, LNAI 9436, https://doi.org/10.1007/978-3-319-25754-9_5
Author Index

Systemvoraussetzungen

Als PDF speichern Als Link merken