Transactions on Rough Sets IX
This book is the ninth volume of the Transactions on Rough Sets series. The 26 papers in it introduce new advances in the foundations and applications of artificial intelligence, engineering, image processing, logic, mathematics, medicine, music, and science.
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| Main Authors | , , |
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| Format | eBook |
| Language | English |
| Published |
Berlin, Heidelberg
Springer Berlin / Heidelberg
2008
Springer Berlin Heidelberg Springer |
| Edition | 1 |
| Series | Lecture Notes in Computer Science |
| Subjects | |
| Online Access | Get full text |
| ISBN | 9783540898757 3540898751 |
| ISSN | 0302-9743 1611-3349 |
| DOI | 10.1007/978-3-540-89876-4 |
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| Abstract | This book is the ninth volume of the Transactions on Rough Sets series. The 26 papers in it introduce new advances in the foundations and applications of artificial intelligence, engineering, image processing, logic, mathematics, medicine, music, and science. |
|---|---|
| AbstractList | This book is the ninth volume of the Transactions on Rough Sets series. The 26 papers in it introduce new advances in the foundations and applications of artificial intelligence, engineering, image processing, logic, mathematics, medicine, music, and science. |
| Author | Unspecified Rybinski, Henryk Skowron, Andrzej |
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| DOI | 10.1007/978-3-540-89876-4 |
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| EISBN | 354089876X 9783540898764 |
| EISSN | 1611-3349 |
| Edition | 1 2008 edition |
| Editor | Rybiński, Henryk Peters, James F. Skowron, Andrzej |
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| RelatedPersons | Kleinberg, Jon M. Mattern, Friedemann Nierstrasz, Oscar Steffen, Bernhard Kittler, Josef Vardi, Moshe Y. Weikum, Gerhard Sudan, Madhu Naor, Moni Mitchell, John C. Terzopoulos, Demetri Pandu Rangan, C. Kanade, Takeo Hutchison, David Tygar, Doug |
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| Snippet | This book is the ninth volume of the Transactions on Rough Sets series. The 26 papers in it introduce new advances in the foundations and applications of... |
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| SubjectTerms | Artificial Intelligence Computation by Abstract Devices Computer Science Data Mining and Knowledge Discovery Mathematical Logic and Formal Languages Models and Principles Rough sets Set theory Theory of Computation |
| TableOfContents | Hierarchical Classifiers for Complex Spatio-temporal Concepts -- Author Index Intro -- Preface -- LNCS Transactions on Rough Sets -- Table of Contents -- Vagueness and Roughness -- Modified Indiscernibility Relation in the Theory of Rough Sets with Real-Valued Attributes: Application to Recognition of Fraunhofer Diffraction Patterns -- On Certain Rough Inclusion Functions -- Automatic Rhythm Retrieval from Musical Files -- FUN: Fast Discovery of Minimal Sets of Attributes Functionally Determining a Decision Attribute -- Information Granulation: A Medical Case Study -- Maximum Class Separability for Rough-Fuzzy C-Means Based Brain MR Image Segmentation -- Approximation Schemes in Logic and Artificial Intelligence -- Decision Rule Based Data Models Using NetTRS System Overview -- A Rough Set Based Approach for ECG Classification -- Universal Problem of Attribute Reduction -- Extracting Relevant Information about Reduct Sets from Data Tables -- Context Algebras, Context Frames, and Their Discrete Duality -- A Study in Granular Computing: On Classifiers Induced from Granular Reflections of Data -- On Classifying Mappings Induced by Granular Structures -- The Neurophysiological Bases of Cognitive Computation Using Rough Set Theory -- Diagnostic Feature Analysis of a Dobutamine Stress Echocardiography Dataset Using Rough Sets -- Rules and Apriori Algorithm in Non-deterministic Information Systems -- On Extension of Dependency and Consistency Degrees of Two Knowledges Represented by Covering -- A New Approach to Distributed Algorithms for Reduct Calculation -- From Information System to Decision Support System -- Debellor: A Data Mining Platform with Stream Architecture -- Category-Based Inductive Reasoning: Rough Set Theoretic Approach -- Probabilistic Dependencies in Linear Hierarchies of Decision Tables -- Automatic Singing Voice Recognition Employing Neural Networks and Rough Sets Classifying Mappings Based on Granulation by Means of \mu_{\rho}^{\varepsilon}(v,u,r) -- Case 2: Results of Tests with Granules of Granular Objects -- Case 3: Results of Tests with Granules of Rules from theTraining Set -- Granulation by Means of Variants of Rough Inclusions Induced by Residual Implications of t-Norms -- Classifying Mappings Based on Granulation by Means of \mu_T^*(u,v,r) -- Case 1: Results of Tests with Granules of Training Objects -- Case 2: Results of Tests with Granules of Rules from the Training Set -- Results of Tests with Granular Objects from the Training Set -- Weighted Voting by Granules of Training Objects According to \mu_{\rho}^{\varepsilon}(u,v,1) -- Conclusions -- References -- The Neurophysiological Bases of Cognitive Computation Using Rough Set Theory -- Introduction -- BasicConcepts -- Objects' (stimuli) Attributes and Classification of the Brain Responses -- Logic of the Anatomical Connections -- Results -- Discussion -- References -- Diagnostic Feature Analysis of a Dobutamine Stress Echocardiography Dataset Using Rough Sets -- Introduction -- Previous Work -- The Dataset -- Results -- Conclusion -- References -- Rules and Apriori Algorithm in Non-deterministic Information Systems -- Introduction -- Basic Definitions and Background of the Research -- Basic Definitions -- An Illustrative Example -- Certain Rule Generation in Non-deterministic Information Systems -- Non-deterministic Information and Incomplete Information -- A Problem of Possible Rule Generation in Non-deterministic Information Systems -- New Criteria: Minimum Support, Minimum Accuracy, Maximum Support and Maximum Accuracy -- Definition of New Criteria -- A Simple Method for Calculating Criteria -- Effective Calculation of Minimum Support and Minimum Accuracy -- Effective Calculation of Maximum Support and Maximum Accuracy Rule Generation by New Criteria in Non-deterministic Information Systems Raw ECG Data Extraction -- Work on ECG Wave Segment Detection and Feature Extraction -- Studies on ECG Classification and Abnormality Detection -- Basics of Electrocardiogram (ECG) -- Ischemic Heart Disease (IHD) -- Classification of MI -- Rough Sets -- Mathematical Basics of Rough-Set Theory -- Rough-Set Description -- Materials and Methods of Analysis -- Development of ECG Data Extraction System -- Removal of Noises from ECG Signals -- Time-Plane Features Extraction -- Development of Knowledge Base -- Development of Inference Engine -- Experimental Results -- Rule Generation -- Calculation of Degree of Dependency (k) -- Conclusion -- References -- Universal Problem of Attribute Reduction -- Introduction -- From Data Table to Decision Table -- Problem of Attribute Reduction -- Definition of Problem -- Examples -- Maximally Discerning Reducts -- Greedy Algorithm -- On Precision of Greedy Algorithm -- On Polynomial Approximate Algorithms -- Lower Bound on R_{\mathrm{min}}(\alpha ) -- Upper Bound on R_{\mathrm{greedy}}(\alpha ) -- Results of Experiments -- Conclusions -- References -- Extracting Relevant Information about Reduct Sets from Data Tables -- Introduction -- OnCoveringof k Attribute Sets by Reducts -- Graphical Representation of Information about the Set of Reducts -- Using Dependencies in Generation of Reducts -- Conclusions -- References -- Context Algebras, Context Frames, and Their Discrete Duality -- Introduction -- Context Algebras and Frames -- Context Logic -- Applications for Formal Concept Analysis -- Intents, Extents and Operations of Concepts -- Dependencies of Attributes -- Implications -- Conclusion -- References -- A Study in Granular Computing: On Classifiers Induced from Granular Reflections of Data -- Introduction: Rough Computing, Rough Inclusions -- Indiscernibility, Granulation by Indiscernibles Generating Candidates for \partial-reducts -- Using Partitions in Fun -- Using Stripped Partitions in Fun -- Using Reduced Stripped Partitions in Fun -- Experimental Results -- Conclusions and Future Work -- References -- Information Granulation: A Medical Case Study -- Introduction -- Self-Organizing System for Information Granulation -- Quality of Information Granules -- Information Granulation in Medical Data -- Description of the Dataset -- Results of the Experiments -- Conclusions -- References -- Maximum Class Separability for Rough-Fuzzy C-Means Based Brain MR Image Segmentation -- Introduction -- Fuzzy C-Means and Rough Sets -- Fuzzy C-Means -- Rough Sets -- Rough-Fuzzy C-Means Algorithm -- Objective Function -- Cluster Prototypes -- Details of the Algorithm -- Segmentation of Brain MR Images -- Feature Extraction -- Selection of Initial Centroids -- Performance Analysis -- Quantitative Indices -- Example -- Haralick's Features Versus Proposed Features -- Random Versus Proposed Initialization Method -- Comparative Performance Analysis -- Conclusion and Future Works -- References -- Approximation Schemes in Logic and Artificial Intelligence -- Introduction -- Abstract Settings for Approximations -- Rough Sets -- Propositional Satisfiability -- Approximating Finite Herbrand Structures -- Knowledge Compilation -- Approximating Semantics for Logic Programs -- Approximating Possible-World Structures -- Minimal Models Reasoning and Repairs in Databases -- Further Work, and Conclusions -- References -- Decision Rule Based Data Models Using NetTRS System Overview -- Introduction -- NetTRS System Architecture -- UserInterface -- The Structure and Content of the Control Script -- The Parameterization of TRS Library Algorithms -- Conclusions and Further Works -- References -- A Rough Set Based Approach for ECG Classification -- Introduction Intro -- Title Page -- Preface -- Organization -- Table of Contents -- Vagueness and Roughness -- Introduction -- Unit Knowledge and Vague Knowledge -- Vague Sets and Rough Sets -- Multiplicity of Membership to a Vague Set -- Operations on Vague Sets -- On Logic of Vague Terms -- FinalRemarks -- References -- Modified Indiscernibility Relation in the Theory of Rough Sets with Real-Valued Attributes: Application to Recognition of Fraunhofer Diffraction Patterns -- Introduction -- Modification of Indiscernibility Relation -- Analysis of Theory of Rough Sets with Discrete Attributes -- Indiscernibility Relation in Rough Sets with Real Valued Attributes -- Application to Fraunhofer Pattern Recognizer -- Optical Foundations -- Enhanced Optimization Method -- PNN Based Classification -- Discussion and Conclusions -- References -- On Certain Rough Inclusion Functions -- Introduction -- The Standard Rough Inclusion Function -- Rough Mereology: A Formal Framework for Rough Inclusion -- In Search of New RIFs -- Mappings Complementary to RIFs -- RIFs and Their Complementary Mappings vs. Similarity and Distance between Sets -- Summary -- References -- Automatic Rhythm Retrieval from Musical Files -- Introduction -- Emulation of Human Perception by Computational Intelligence Techniques -- Experiments -- Database -- ANN-Based Experiment -- Rough Set-Based Experiments -- Automatic Drum Accompaniment Application -- Algorithm Complexity -- Concluding Remarks -- References -- FUN: Fast Discovery of Minimal Sets of Attributes Functionally Determining a Decision Attribute -- Introduction -- BasicNotions -- Information Systems -- Functional Dependencies -- Decision Tables, Reducts and Functional Dependencies -- Computing Minimal Sets of Attributes Functionally Determining Given Dependent Attribute with Fun -- Main Algorithm Mereology vs. Rough Sets -- Rough Mereology and Rough Inclusions -- Granulation of Knowledge: The Abstract Definition of a Granule of Knowledge -- Granules of Knowledge -- Granular Reflections of Decision Systems -- Classifiers: Rough Set Methods -- The Łukasiewicz Rough Inclusion and Granules -- The Setting of Experiments -- Results of Experiments: Training Table=Test Table -- Lymphography Data Set -- Heart Disease Data Set -- Results of Experiments with Train-and-Test in 1:1 Ratio -- Primary Tumor Data Set -- Diabetes Data Set -- Effect of Granule Selection on Classification -- Experiments: Cross-Validation CV-10 -- Concept-Dependent Granulation -- Experiments with Rough Inclusions from Residua of t-Norms and Extensions of μL -- Results of Tests with Granules of Training Objects According to \mu_{\rho}^{\varepsilon}(v,u,1) Voting for Decision -- Results of Tests with Granules of Training Objects According to \mu_{\rho}^{\varepsilon}(v,u,r) -- Rough Inclusions and Their Weaker Variants Obtained from Residual Implications in Classification of Data -- Conclusions -- References -- On Classifying Mappings Induced by Granular Structures -- Introduction: Rough Sets, Rough Inclusions, Granulation of Knowledge -- Indiscernibility, Granulation of Knowledge -- Rough Inclusions -- Rough Inclusions and Granules from Metrics -- A Graded Variant of \mu_{\rho} -- Granular Reflections of Granules and Granulated Data Sets -- Mapping Granules of Objects or Rules on Decision Values by Variants of μH -- Classifying Mappings Induced by \mu_{\rho}^{\varepsilon}(u,v,1) -- Case 2: Results of Tests with Granules of Granular Objects -- Case 3: Results of Tests with Granules of Rules from the Training Set -- Case 4: Results of Tests with Granules of Rules from the Granulated Training Set |
| Title | Transactions on Rough Sets IX |
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