Rough Sets and Knowledge Technology

Name: Rough Sets and Knowledge Technology | 7th International Conference, RSKT 2012, Chengdu, China, August 17-20, 2012, Proceedings
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7th International Conference, RSKT 2012, Chengdu, China, August 17-20, 2012, Proceedings

Tianrui Li Hung Son Nguyen Guoyin Wang Jerzy W. Grzymala-Busse Ryszard Janicki Aboul Ella Hassanien Hong Yu(Herausgeber*in)

Springer (Verlag)

Erschienen am 27. Juli 2012

XVIII, 526 Seiten

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Title
Preface
Organization
Table of Contents
Part I: Rough Sets and Its Generalizations
A Characterization of Rough Separability
Introduction
Rough Sets and Indiscernibility Relations
Rough Separability and Pseudometric Spaces
Pseudometrics Determined by Families of Sets
Equivalence of Pseudometric Spaces
Conclusions
References
Data-Driven Valued Tolerance Relation
Introduction
Valued Tolerance Relation
Data-Driven Valued Tolerance Relation
Calculation of Tolerance Degree
Auto-selection of Threshold
Experiment Results
Conclusions
References
Optimistic Multi-Granulation Fuzzy Rough Set Model Based on Triangular Norm
Introduction
The Rough Set Based on Triangular Norm
Optimistic Multi-Granulation Fuzzy Rough Set Based on Triangular Norm
Conclusions
References
Rough Set Model Based on Hybrid Tolerance Relation
Introduction
Preliminaries
Strong and Weak Indiscernibility Relations
Incomplete Information System and Tolerance Relations
Weak Tolerance Relations
Hybrid Tolerance Relation and Decision Rule
Hybrid Tolerance Relation
Decision Rule
Conclusions
References
Scalable Improved Quick Reduct: Sample Based
Introduction
Overview of IQRA and IQRA_IG Algorithms
Proposed SGIQRA_IG Algorithm
Experiments and Results
Analysis of Results
Conclusion
References
Soft Rough Sets Based on Similarity Measures
Introduction
Preliminaries
Analysis of Soft Rough Approximations
Soft Fuzzy Rough Sets Based on Similarity Measures
Concluding Remarks
References
Theory of Truth Degrees in Three Valued Formed System RSL
Introduction
Basic of Rough Set and Formed System RSL
The Theory of Truth Degree
Similarity Degree and a Pseudo-metric among Formulas
Conclusion
References
Upper Approximation Reduction Based on Intuitionistic Fuzzy T Equivalence Information Systems
Introduction
Intuitionistic Fuzzy Rough Sets and Intuitionistic Fuzzy T Equivalence Information Systems
Upper Approximation Reduction in IF T Equivalence Information Systems with Decision
An Illustrated Example
Conclusions
References
Part II: Rough Sets in Data and Knowledge Processing
A Fuzzy-Rough Sets Based Compact Rule Induction Method for Classifying Hybrid Data
Introduction
Preliminary Notion of Rough Sets Based Rule Induction Method
Fuzzy Rough Sets Based Compact Rule Learner
Experiments and Discussion
Conclusion
References
A New Intuitionistic Fuzzy Rough Set Approach for Decision Support
Introduction
Preliminaries
Dominance-Based Rough Set Approach
Intuitionistic Fuzzy Theory
Uncertain Rule Induction
Believe Factor
Measurements
Believable Rule Induction
Illustrative Example
Decision Table and Rough Approximations
Believable Rule Induction
Verification of Sorting Capability
Conclusion
References
A New Rule Induction Method from a Decision Table Using a Statistical Test
Introduction
Model of Data Generation
Proposal of Rule Estimation Method by Statistical Test
Algorithm for Rule Induction
Experimental Studies
Conclusions
References
A Rough Neurocomputing Approach for Illumination Invariant Face Recognition System
Introduction
Rough Set Theory (RST)
Proposed RNRS Architecture
Design of ADNN
Approximation Neuron
Decider Neuron
Architecture of ADNN
Experimental Results
Conclusion
References
An Approximation Decision Entropy Based Decision Tree Algorithm and Its Application in Intrusion Detection
Introduction
Preliminaries
Approximation Decision Entropy
Decision Tree Algorithm DTADE
Experimental Results
Conclusion
References
Application of Rough Set Theory to Prediction of Antimicrobial Activity of Bis-quaternary Ammonium Chlorides
Introduction
Material and Methods
Material
Discovery of Decision Rules Using DRSA
Attribute Relevance
Results and Discussion
References
Classification and Decision Based on Parallel Reducts and F-Rough Sets
Introduction
F-Rough Sets and Parallel Reducts
Three Types of Classification(Decision) Based on Parallel Reducts and F-Rough Sets
Specific Decision Subsystem
Decision Subsystem Selected Randomly
Deciding by a Majority Vote
Conclusion
References
Evidential Clustering or Rough Clustering: The Choice Is Yours
Introduction
Similarities between ECM and RKM
Strengths of ECM
Strengths of RKM
Conclusion
References
Heuristic for Attribute Selection Using Belief Discernibility Matrix
Introduction
Rough Set Theory
Belief Function Theory
Heuristic for Attribute Selection Method Using Belief Discernibility Matrix
Uncertain Decision Table
Feature Selection with Johnson´s Heuristic Algorithm
Belief Discernibility Matrix
Belief Rough Set Classifier
Experimental Results
Conclusion and Future Work
References
Incremental Rules Induction Based on Rule Layers
Introduction
Rough Sets and Probabilistic Rules
Rough Set Theory
Probabilistic Rules
Problems in Incremental Rule Induction
Theory for Incremental Learning
Both: Negative
R: Positive
d: Positive
d: Positive
Updates of Accuracy and Coverage
An Algorithm for Incremental Learning
Algorithm
Experimental Results
Conclusion
References
Optimization of Inhibitory Decision Rules Relative to Length and Coverage
Introduction
Nonredundant Decision Rules
Directed Acyclic Graph (T)
Procedures of Optimization Relative to Length and Coverage
Experimental Results
Conclusions
References
Parallelized Computing of Attribute Core Based on Rough Set Theory and MapReduce
Introduction
Parallel Algorithm for Computing Attribute Core Based on Rough Set and MapReduce
Experiments and Discussion
Case Study
Comparative Experiments for Efficiency of the Proposed Algorithm
Conclusion
References
Semi-supervised Vehicle Recognition: An Approximate Region Constrained Approach
Introduction
Semi-supervised Learning Based on Approximate Region Constraints (SsL-ARC)
SsL-ARC Algorithm Framework
Approximate Region Constraints
Experiments
Conclusions
References
Part III: Knowledge Technology
A Color Image Segmentation Algorithm by Integrating Watershed with Region Merging
Introduction
Watershed Algorithm
Merger of the Image Regions
Distance Metric between Graph Regions
Merger Algorithm of the Image Regions
Experimental Results and Analysis
Conclusion
References
A Mixed Strategy Multi-Objective Coevolutionary Algorithm Based on Single-Point Mutation and Particle Swarm Optimization
Introduction
Multi-Objective Optimization Problems
Multi-Objective Coordination Operator Evolutionary Programming Based on Single Point and PSO Operator
Mixed Strategy
Maintenance of External Archive
Algorithm Description
The Experimental Results
Experiment Setting
Results and Analysis
Summary
References
A Novel Distributed Machine Learning Method for Classification: Parallel Covering Algorithm
Introduction
Covering Algorithm Overview
Learn Procedure
Classification
Parallel Covering Algorithm
Experiments and Analysis
Uniprocessor Environment
Multi-core Parallel Computing Environment
Conclusion
References
A Spatial Clustering Method for Points-with-Directions
Introduction
Related Work
Points-with-Directions Clustering
Problem Definition
Spatial Clustering Method for Points-with-Directions
The Limitations of PDC
PDC+
Experimental Evaluation
Conclusions and Future Work
References
An Argumentation Framework for Non-monotonic Reasoning in Description Logic
Introduction
The Description Logic ALC
Defeasible ALC
Inductive Terminologies
Conclusion and Future Work
References
Analysis of Symmetry Properties for Bayesian Confirmation Measures
Introduction
Preliminaries
Property of Bayesian Confirmation
Properties of Symmetry
A New Set of Symmetry Properties
Conclusions
References
Applying Verbal Decision Analysis in Selecting Specific Practices of CMMI
Introduction
Verbal Decision Analysis: Overview and Application
Conclusions, Future Works and Acknowledgment
References
Belief Networks in Classification of Laryngopathies Based on Speech Spectrum Analysis
Introduction
Laryngopathy Data
Theoretical Foundations of BeliefSEEKER
Experiments
Conclusions
References
Comparing Similarity of Concepts Identified by Temporal Patterns of Terms in Biomedical Research Documents
Introduction
A Method for Analyzing Distances on Taxonomy and Temporal Patterns of Term Usage
Obtaining Temporal Patterns of Data-Driven Indexes Related to Term Usages
Defining Similarity of Terms on a Structured Taxonomy
Analyzing Similarity of Terms in Temporal Patterns of Medical Research Documents
Analysis of Disease over Time
Obtaining Temporal Patterns of Medical Terms about Migraine Drug Therapy Studies
Similarity of the Terms in Obtained Temporal Patterns on MeSH
Conclusion
References
Extracting Incidental and Global Knowledge through Compact Pattern Trees in Distributed Environment
Introduction
An Overview of Compact Pattern Tree
Global Rules from Distributed Compact Pattern Tree
Extraction of Local and Global Rules
Algorithms and Illustrations of Proposed Methodology
Experimental Results
Conclusion
References
Hierarchical Path-Finding Based on Decision Tree
Introduction
Related Work
Problem Formulation
Influence of Terrain to A*
Map Division Is a Classification Problem
Hierarchical Path-Finding Based on Decision Tree
Handing Continuous-Valued Attributes
Stopping Criterion
Attribute Selection Measure
Online Path-Finding
Empirical Study
Conclusions and Future Work
References
Human Activity Recognition with Trajectory Data in Multi-floor Indoor Environment
Introduction
Related Work
Problem and Challenges
Human Activity Recognition from Trajectory Data
Semantic Indoor Trajectory Model
Activity Recognition Model
Experiments and Evaluation
Conclusions
References
Remote Sensing Image Data Storage and Search Method Based on Pyramid Model in Cloud
Introduction
Background
Multiresolution Pyramid and Image Block Technology
File Organization and Programming Model in Cloud
Related Work of Massive Image Data
The Image Data Storage and Search Method Based on MapReduce
The MapReduce Programming Paradigm
Tiles Encoding Method
Mapping Method between Tile Layers
Process of Image Data Storage and Search
Algorithm Complexity Analysis
Experiment Environment and Datasets Analysis
Experiment Environment and Datasets
The I/O Performance Test
Conclusion
References
Learning to Classify Service Data with Latent Semantics
Introduction
Our Approach
Evaluation
Dataset Description
Comparison of Clustering Methods
Analysis of Multiclass Classification Performance
Discussion
Conclusions
References
Link Communities Detection via Local Approach
Introduction
Community Detection
Problem Definition
LBLC Algorithm
Local Based Link Community Detection (LBLC)
Description of LBLC Algorithm
Evaluation of Link Community
Experiments
Computer-Generated Networks
Real-World Networks
Conclusion
References
Maximizing Influence Spread in a New Propagation Model
Introduction
Layered Network and Layered Cascade Model
Layered Network
Layered Cascade Model
Information Propagation in Network
Role of Edges in Networks
Information Propagation in LC Model
Influence Maximization Problem
Experiment
Experiment Setup
Experiment Results
Discussion on the Results
Conclusions
References
Semi-supervised Clustering Ensemble Based on Multi-ant Colonies Algorithm
Introduction
Ant Colony Clustering with Pairwise Constraints
Semi-supervised Clustering Ensemble
Experimental Results
Conclusion
References
Semi-supervised Hierarchical Co-clustering
Introduction
Feature Clustering
Similarity Measurement
Hierarchical Co-clustering
Experiments Evaluation
Data Preprocessing
Experiment Process
Experiment Results
Conclusion
References
Part IV: Workshop: Advances in Granular Computing, 2012 (AGC2012)
A Granular Computing Perspective on Image Organization within an Image Retrieval Context
Introduction
Fundamentals of Image Retrieval
Granular Organization of Images
Feature Vector Generation
Similarity-Based Image Organization
Hierarchical Structure of Image Space
Granular Interaction Mechanisms
Support for Intelligent Decision-Making
Conclusion
References
Granular Computing in Opinion Target Extraction
Introduction
Preliminaries
Algorithm
The Algorithm to Compute the Features of the Words
Extracting the Opinion Target with Neural Network
Experiment
Data Set
Experiment Result
Conclusion
References
Granular Covering Selection Methods Dependent on the Granule Size
Introduction
Granular Rough Computing
Covering Finding Methods
Covering by Granules with Minimal Size
Covering by Granules with Average Size
Covering by Granules of Maximal Size
Experimental Session
Conclusions
References
Rough Set Approximations in Incomplete Multi-scale Information Systems
Introduction
Classical Rough Sets and Information Systems
Pawlak Rough Sets
Information Systems
Multi-scale Information Systems
Complete Multi-scale Information Systems
Incomplete Multi-scale Information Systems
Properties of Rough Set Approximations
Conclusion
References
Three Granular Structure Models in Graphs
Purpose of the Study
Related Studies
Granules and Granular Structures
Three Granular Structure Models in Graphs
Vertex-Oriented Granular Structure Model in Graphs
Edge-Oriented Granular Structure Model in Graphs
Combination-Oriented Granular Structure Model in Graphs
Conclusion
References
Part V: Special Session: Decision-Theoretic Rough Set Model and Applications
A Competition Strategy to Cost-Sensitive Decision Trees
Introduction
Related Works
The Data Model
Computation of the Average Cost
Related Algorithms
New Algorithms
-ID3
The Post-prune Technique
The Competition Strategy
Experiments
Conclusions and Further Works
References
An Information-Theoretic Interpretation of Thresholds in Probabilistic Rough Sets
Introduction
An Overview of Probabilistic Rough Sets
Three Probabilistic Regions Defined by a Pair of Thresholds
Thresholds and Classification Errors and Costs
Determination of Thresholds
An Information-Theoretic Interpretation
Shannon Entropy of a Partition
Uncertainties of Three Probabilistic Regions
Determining Optimal Thresholds by Entropy Minimization
An Example
Conclusion
References
Cost-Sensitive Classification Based on Decision-Theoretic Rough Set Model
Introduction
Decision-Theoretic Rough Set Model
Cost-Sensitive Classification Based on DTRS
Experimental Analysis
Conclusion
References
Decision-Theoretic Rough Sets with Probabilistic Distribution
Introduction
Preliminaries
Probabilistic Distribution
Decision-Theoretic Rough Sets
Extension of Decision-Theoretic Rough Set Models
Decision-Theoretic Rough Sets with Uniform Distribution
Decision-Theoretic Rough Sets with Normal Distribution
An Illustration
Conclusions
References
Multiple Criteria Decision Analysis with Game-Theoretic Rough Sets
Introduction
Overview of Game-Theoretic Rough Sets
The GTRS Based Framework for Multiple Criteria Decision Analysis
Multiple Criteria as Players in a Game
Strategies in Multiple Criteria Analysis
Payoff Functions for Analyzing Strategies
Implementing Competition for Effective Solutions
A Confidence versus Coverage Game Example
Conclusion
References
Part VI: Special Session: Intelligent Decision-Making and Granular Computing
Granularity Analysis of Fuzzy Soft Set
Introduction
Preliminaries
-Dominance Class on the Possibility Degree of FSs
Standardized Methods of the Fuzzy Soft Set
The Possibility Degree of FSs
-Dominance Class and -Covering Approximation Space
Variable Precision Granularity Analysis Based on -Covering Approximation Space
Illustrative Examples
Conclusions
References
Granular Approach in Knowledge Discovery
Introduction
Granular Computing Approach in Solving the Problem
Description of the Method
Granules Generation
The Blockage Prediction
Experimental Verification
Conclusions
References
Hybrid: A New Multigranulation Rough Set Approach
Introduction
Multigranulation Rough Sets
Optimistic Multigranulation Rough Set
Pessimistic Multigranulation Rough Set
Hybrid Multigranulation Approach to Rough Set
Conclusions
References
Inclusion Degrees of Graded Ill-Known Sets
Introduction
Graded Ill-Known Sets
Inclusion Degrees of Graded Ill-Known Sets
A Simple Example
References
Information Granularity and Granular Structure in Decision Making
Introduction
Weights Acquisition Based on Information Granularity
The Partial Granular Structure in Preference Relation
Multiplicative Preference Relation and Fuzzy Preference Relation
Partial Granular Structure
Conclusions
References
Semi-supervised Clustering Ensemble Based on Collaborative Training
Introduction
Related Work
Clustering Ensemble
Semi-supervised Learning
Collaborative Training
Semi-supervised Clustering Ensemble Model Based on Collaborative Training
Base Clustering Components Generation
Using Tri-training as Consensus
Semi-supervised Clustering Ensemble Model Based on Collaborative Training
Unsupervised Clustering Ensemble Model Based on Collaborative Training
Experiments
Dataset
Experiment Methodology
Results
Conclusion
References
Soft Set Approaches to Decision Making Problems
Introduction
Soft Sets and Fuzzy Soft Sets
Analysis of the Existing Approaches
Choice Value Based Approach
Comparison Score Based Approach
Approaches for Incomplete Information
Conclusions
References
The Models of Variable PrecisionMultigranulation Rough Sets
Introduction
VPRS and MGRS
Variable Precision Rough Set
Multigranulation Rough Set
Variable Precision Multigranulation Rough Sets
Variable Precision Optimistic Multigranulation Rough Sets
Variable Precision Pessimistic Multigranulation Rough Sets
Relationships among Several Models
Conclusions
References
Topological Properties of the Pessimistic Multigranulation Rough Set Model
Introduction
Review of Multigranulation Rough Set Model
Topological Properties of the Pessimistic Multigranulation Rough Set Model
Concluding Remarks
References
Part VII: Special Session: Rough Set Foundations
Axiomatic Granular Approach to Knowledge Correspondences
Introduction
General Rough Y-Systems - A First Order Approach
Sub-Natural Correspondences (SNC) across RYS
References
Definable and Rough Sets in Covering-Based Approximation Spaces
Introduction
Definable Sets and Their Algebraic Structure
Approximations of Sets
Comparison with Other Neighborhood Based Operators
Rough Sets and Their Algebraic Structure
Conclusions
References
Intuitionistic Fuzzy Topologies in Crisp Approximation Spaces
Introduction
Basic Notions of Intuitionistic Fuzzy Sets
Rough Intuitionistic Fuzzy Sets
Basic Concepts of Intuitionistic Fuzzy Topological Spaces
Intuitionistic Fuzzy Topologies of Rough Intuitionistic Fuzzy Sets
Conclusion
References
Oppositions in Rough Set Theory
Introduction
Preliminary Notions
Oppositions and Geometrical Organization
Rough Sets
Opposition from Approximations
Opposition from Relations
Tetrahedron from Two Relations
Conclusions
References
Partial First-order Logic with Approximative Functors Based on Properties
Introduction
Tool-based Partial First-order Logic (TbPFoL) with Approximative Functors
Language of TbPFoL with Approximative Functors
Interpretations of TbPFol with Approximation Functors
Generated Tool-based General Partial Approximation Spaces
Semantic Rules of TbPFoL with Approximative Functors
Theorems of Representations
Conclusion
References
Author Index

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