Rough Sets and Knowledge Technology
7th International Conference, RSKT 2012, Chengdu, China, August 17-20, 2012, Proceedings
Published on 27. July 2012
XVIII, 526 pages
978-3-642-31900-6 (ISBN)
Description
This book constitutes the refereed proceedings of the 7th International Conference on Rough Sets and Knowledge Technology, RSKT 2012, held in Chengdu, China during August 2012, as one of the co-located conferences of the 2012 Joint Rough Set Symposium, JRS 2012. The 63 revised papers (including 42 regular and 21 short papers) were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on rough sets and its generalizations, rough sets in data and knowledge processing, knowledge technology, advances in granular computing (AGC 2012 workshop), decision-theoretic rough set model and applications (special session), intelligent decision making and granular computing (special session), rough set foundations (special session).
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Tianrui Li | Hung Son Nguyen | Guoyin Wang
Rough Sets and Knowledge Technology
7th International Conference, RSKT 2012, Chengdu, China, August 17-20, 2012, Proceedings
Book
08/2012
Springer
€53.49
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Content
- 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
