E-Book, Englisch, 284 Seiten
Jian / Liu / Lin Hybrid Rough Sets and Applications in Uncertain Decision-Making
1. Auflage 2010
ISBN: 978-1-4200-8749-9
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)
E-Book, Englisch, 284 Seiten
Reihe: Systems Evaluation, Prediction, and Decision-Making
ISBN: 978-1-4200-8749-9
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)
As a powerful approach to data reasoning, rough set theory has proven to be invaluable in knowledge acquisition, decision analysis and forecasting, and knowledge discovery. With the ability to enhance the advantages of other soft technology theories, hybrid rough set theory is quickly emerging as a method of choice for decision making under uncertain conditions.
Keeping the complicated mathematics to a minimum, Hybrid Rough Sets and Applications in Uncertain Decision-Making provides a systematic introduction to the methods and application of the hybridization for rough set theory with other related soft technology theories, including probability, grey systems, fuzzy sets, and artificial neural networks. It also:
- Addresses the variety of uncertainties that can arise in the practical application of knowledge representation systems
- Unveils a novel hybrid model of probability and rough sets
- Introduces grey variable precision rough set models
- Analyzes the advantages and disadvantages of various practical applications
The authors examine the scope of application of the rough set theory and discuss how the combination of variable precision rough sets and dominance relations can produce probabilistic preference rules out of preference attribute decision tables of preference actions. Complete with numerous cases that illustrate the specific application of hybrid methods, the text adopts the latest achievements in the theory, method, and application of rough sets.
Zielgruppe
Computer scientsits, mathematicians, geoscientists, engineers, agriculturalists, and bioscientsts.
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
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, 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
ß-Rough Approximation
Classification Quality and ß-Reduct
Discussion about ß 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 Set
Bayes’ Probability
Consistent Degree, Coverage, 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
ß-Rough Approximation Based on Dominance Relations
Classification Quality and Approximate Reduct
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
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