Hybrid rough sets and applications in uncertain decision-making [electronic resource] / Lirong Jian, Sifeng Liu, Yi Lin.
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Full Text (via Taylor & Francis) |
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| Other Authors: | , |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Boca Raton, FL :
Auerbach Publications,
©2011.
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| Series: | Systems evaluation, prediction, and decision-making series.
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| Subjects: |
Table of Contents:
- Introduction ; Background and Significance of Soft Computing Technology; Analytical Method of Data Mining; Automatic Prediction of Trends and Behavior; Association Analysis; Cluster Analysis; Concept Description; Deviation Detection; Knowledge Discovered by Data Mining; Characteristics of Rough Set Theory and Current Status of Rough Set Theory Research Characteristics of the Rough Set Theory; Current Status of Rough Set Theory Research; Analysis with.
- Decision-Making; Non-Decision-Making Analysis; Hybrid of Rough Set Theory and Other Soft Technologies; Hybrid of Rough Sets and Probability Statistics; Hybrid of Rough Sets and Dominance Relation; Hybrid of Rough Sets and Fuzzy Sets; Hybrid of Rough Set and Grey System Theory; Hybrid of Rough Sets and Neural Networks Rough Set Theory ; Information Systems and Classification; Information Systems and Indiscernibility Relation; Set and Approximations of Set; Attributes Dependence and Approximation Accuracy; Quality of Approximation and Reduct; Calculation of the Reduct and Core of Information System Based on Discernable Matrix; Decision.
- Table and Rule Acquisition; The Attribute Dependence, Attribute Reduct, and Core; Decision Rules; Use the Discernibility Matrix to Work Out Reducts, Core,
- Set Bayes' Probability; Consistent Degree, Coverage,
- Preferential Probabilistic Decision Rules; Algorithm Design; An Application Case; Post Evaluation of Construction Projects Based on Dominance-Based Rough Set Construction of Preferential Evaluation Decision Table; Search of Reduct and Establishment of Preferential Rules; Performance Evaluation of Discipline Construction in Teaching-Research Universities Based on Dominance-Based Rough Set The Basic Principles of the Construction of Evaluation Index System The Establishment of Index System and Determination of Weight and Equivalent; Data Collection and.
- Pretreatment Data Discretization; Search of Reducts and Generation of Preferential Rules; Analysis of Evaluation Results Hybrid of Rough Set Theory and Fuzzy Set Theory; The Basic Concepts of the Fuzzy Set Theory; Fuzzy Set and Fuzzy Membership Function; Operation of Fuzzy Subsets; Fuzzy Relation and Operation; Synthesis of Fuzzy Relations; λ-Cut Set and the Decomposition Proposition; The Fuzziness of Fuzzy Sets and Measure of Fuzziness; Rough Fuzzy Set and Fuzzy Rough Set; Rough Fuzzy Set; Fuzzy Rough Set; Variable Precision Rough Fuzzy.
- and Support; Probability Rules; Approach to Obtain Probabilistic Rules Hybrid of Rough Set and Dominance Relation Hybrid of Rough Set and Dominance Relation; Dominance-Based Rough Set; The Classification of the Decision Tables with Preference Attribute Dominating Sets and Dominated Sets; Rough Approximation by Means of Dominance Relations; Classification Quality and Reduct; Preferential Decision Rules; Dominance-Based Variable Precision Rough Set; Inconsistency and Indiscernibility Based on Dominance Relation β-Rough Approximation Based on Dominance Relations; Classification Quality and Approximate Reduct.
- and Decision Rules of Decision Table; Data Discretization; Expert Discrete Method; Equal Width Interval Method and Equal Frequency Interval Method; The Most Subdivision Entropy Method; Chimerge Method; Common Algorithms of Attribute Reduct; Quick Reduct Algorithm; Heuristic Algorithm of Attribute Reduct; Genetic Algorithm; Application Case; Data Collecting and Variable Selection; Data Discretization; Attribute Reduct; Rule Generation; Simulation of the Decision Rules Hybrid of Rough Set Theory and Probability ; Rough Membership Function; Variable Precision Rough Set Model; β-Rough.
- Sets; Rough Membership Function Based on λ-Cut Set; The Rough Approximation of Variable Precision Rough Fuzzy Set The Approximate Quality and Approximate Reduct of variable Precision; &
- Approximation; Classification Quality and β-Reduct; Discussion about β Value; Construction of Hierarchical Knowledge Granularity Based on VPRS Knowledge Granularity; Relationship between VPRS and Knowledge Granularity; Approximation and Knowledge Granularity; Classification Quality and Granularity Knowledge Granularity Construction of Hierarchical Knowledge Granularity; Methods of Construction of Hierarchical Knowledge Granularity Algorithm Description; Methods of Rule Acquisition Based on the Inconsistent Information System in Rough.