《不完備信息系統及粗糙集理論:模型與屬性約簡(英文版)》主要介紹粗糙集理論及其在不完備信息系統中的拓展。粗糙集理論是用於處理不精確問題的數學理論，是經典集合論的重要發展。由於粗糙集理論建立在等價關係的基礎上，因而不能用來處理具有未知屬性值的不完備信息系統，因而如何在不完備信息系統中擴展粗糙集的相關概念對於粗糙集的發展具有極其重要的意義。 《不完備信息系統及粗糙集理論:模型與屬性約簡(英文版)》力圖概括國內外最新的研究成果，主要內容有：粗糙集理論的基本概念、具有遺漏型和缺席型未知屬性值的不完備信息系統及相關擴展模型，鄰域系統及不完備信息系統中基於鄰域系統的粗糙集模型與屬性約簡方法，不完備信息系統中的序關係粗糙集模型及序關係模糊粗糙集模型，集值信息系統和區間值信息系統中的擴展粗糙集模型等。
基本介紹
- 書名：不完備信息系統及粗糙集理論:模型與屬性約
- 作者：楊習貝 楊靜宇
- 出版日期：2011年11月1日
- 語種：英語
- ISBN：9787030324764
- 品牌：科學出版社
- 外文名：Incomplete Information System and Rough Set Theory Models and Attribute Reductio
- 出版社：科學出版社
- 頁數：232頁
- 開本：16
- 定價：80.00
內容簡介,圖書目錄,
內容簡介
《不完備信息系統及粗糙集理論:模型與屬性約簡(英文版)》由科學出版社出版。
圖書目錄
Part I Indiscernibility Relation Based Rough Sets
Chapter 1 indiscernibility Relation, Rough Sets and Information System
1.1 Pawlak's Rough Approximation
1.1.1 Rough Set
1.1.2 Uncertainty Measurements and Knowledge Granulation
1.1.3 Knowledge Reductions
1.1.4 Knowledge Dependency
1.2 Variable Precision Rough Set
1.2.1 Inclusion Error and Variable Precision Rough Set
1.2.2 Several Reducts in Variable Precision Rough Set
1.3 Multigranulation Rough Set
1.3.1 Optimistic Multigranulation Rough Set
1.3.2 Pessimistic Multigranulation Rough Set
1.3.3 Multigranulation Rough Memberships
1.4 Hierarchical Structures on Multigranulation Spaces
1.4.1 Definitions of Three Hierarchical Structures
1.4.2 Relationships Between Hierarchical Structures and Multigranulation Rough Sets
1.5 Information System
1.5.1 Information System and Rough Set
1.5.2 Rough Sets in Multiple-source Information Systems
1.5.3 Several Reducts in Decision System
1.6 Conclusions
References
Part II Incomplete Information Systems and Rough Sets
Chapter 2 Expansions of Rough Sets in Incomplete Information Systems..
2.1 Tolerance Relation Based Rough Set Approach
2.1.1 Tolerance Relation and Its Reducts
2.1.2 Tolerance Relation Based Rough Set and Generalized Decision Reduct
2.2 Valued Tolerance Relation Based Rough Set Approach
2.2.1 Valued Tolerance Relation
2.2.2 Valued Tolerance Relation Based Fuzzy Rough Set
2.3 Maximal Consistent Block Based Rough Set Approach
2.3.1 Maximal Consistent Block and Its Reducts
2.3.2 Maximal Consistent Block Based Rough Set and Approximate Distribution Reducts
2.4 Descriptor Based Rough Set
2.4.1 Descriptor and Reduct Descriptor
2.4.2 Descriptor Based Rough Set and Generalized Decision Reduct of Descriptor
2.5 Similarity Relation Based Rough Set Approach
2.5.1 Similarity Relation and Similarity Based Rough Set
2.5.2 Approximate Distribution Reducts in Similarity Relation Based Rough Set
2.6 Difference Relation Based Rough Set Approach
2.6.1 Difference Relation and Its Reducts
2.6.2 Rough Set Based on Difference Relation
2.6.3 Approximate Distribution Reducts in Difference Relation Based Rough Set
2.7 Limited Tolerance Relation Based Rough Set Approach
2.7.1 Limited Tolerance Relation
2.7.2 Limited Tolerance Relation Based Rough Set
2.8 Characteristic Relation Based Rough Set Approach
2.8.1 Characteristic Relation and Characteristic Relation Based Rough Set
2.8.2 Approximate Distribution Reducts in Characteristic Relation Based Rough Set
2.9 Conclusions
References
Chapter 3 Neighborhood System and Rough Set in Incomplete Information System
3.1 Neighborhood System
3.1.1 From Granular Computing to Neighborhood System
3.1.2 Binary Neighborhood System
3.1.3 Covering and Neighborhood System
3.1.4 Fuzzy Neighborhood System
3.1.5 Neighborhood System and Topological Space
3.1.6 Knowledge Operation in Neighborhood System
3.2 Neighborhood System and Rough Approximations
3.2.1 Neighborhood System Based Rough Sets
3.2.2 Relationship Between Neighborhood System Based Rough Set and VPRS
3.2.3 Neighborhood System Based Rough Approximations in Incomplete Information System
3.3 Reducts Neighborhood Systems
3.3.1 Reducts Neighborhood Systems in Incomplete Information System
3.3.2 Neighborhood Systems Based Approximate Distribution Reducts
3.4 Conclusions
References
Part III Dominance-based Rough Sets and Incomplete Information Systems
Chapter 4 Dominance-based Rough Sets in "," Incomplete Information System
Chapter 5 Dominance-based Rough Sets in "？" Incomplete Information System
Part IV Incomplete Information Systems and Multigranulation Rough Sets
Chapter 6 Multigranulation Rough Sets in Incomplete Information System
Chapter 1 indiscernibility Relation, Rough Sets and Information System
1.1 Pawlak's Rough Approximation
1.1.1 Rough Set
1.1.2 Uncertainty Measurements and Knowledge Granulation
1.1.3 Knowledge Reductions
1.1.4 Knowledge Dependency
1.2 Variable Precision Rough Set
1.2.1 Inclusion Error and Variable Precision Rough Set
1.2.2 Several Reducts in Variable Precision Rough Set
1.3 Multigranulation Rough Set
1.3.1 Optimistic Multigranulation Rough Set
1.3.2 Pessimistic Multigranulation Rough Set
1.3.3 Multigranulation Rough Memberships
1.4 Hierarchical Structures on Multigranulation Spaces
1.4.1 Definitions of Three Hierarchical Structures
1.4.2 Relationships Between Hierarchical Structures and Multigranulation Rough Sets
1.5 Information System
1.5.1 Information System and Rough Set
1.5.2 Rough Sets in Multiple-source Information Systems
1.5.3 Several Reducts in Decision System
1.6 Conclusions
References
Part II Incomplete Information Systems and Rough Sets
Chapter 2 Expansions of Rough Sets in Incomplete Information Systems..
2.1 Tolerance Relation Based Rough Set Approach
2.1.1 Tolerance Relation and Its Reducts
2.1.2 Tolerance Relation Based Rough Set and Generalized Decision Reduct
2.2 Valued Tolerance Relation Based Rough Set Approach
2.2.1 Valued Tolerance Relation
2.2.2 Valued Tolerance Relation Based Fuzzy Rough Set
2.3 Maximal Consistent Block Based Rough Set Approach
2.3.1 Maximal Consistent Block and Its Reducts
2.3.2 Maximal Consistent Block Based Rough Set and Approximate Distribution Reducts
2.4 Descriptor Based Rough Set
2.4.1 Descriptor and Reduct Descriptor
2.4.2 Descriptor Based Rough Set and Generalized Decision Reduct of Descriptor
2.5 Similarity Relation Based Rough Set Approach
2.5.1 Similarity Relation and Similarity Based Rough Set
2.5.2 Approximate Distribution Reducts in Similarity Relation Based Rough Set
2.6 Difference Relation Based Rough Set Approach
2.6.1 Difference Relation and Its Reducts
2.6.2 Rough Set Based on Difference Relation
2.6.3 Approximate Distribution Reducts in Difference Relation Based Rough Set
2.7 Limited Tolerance Relation Based Rough Set Approach
2.7.1 Limited Tolerance Relation
2.7.2 Limited Tolerance Relation Based Rough Set
2.8 Characteristic Relation Based Rough Set Approach
2.8.1 Characteristic Relation and Characteristic Relation Based Rough Set
2.8.2 Approximate Distribution Reducts in Characteristic Relation Based Rough Set
2.9 Conclusions
References
Chapter 3 Neighborhood System and Rough Set in Incomplete Information System
3.1 Neighborhood System
3.1.1 From Granular Computing to Neighborhood System
3.1.2 Binary Neighborhood System
3.1.3 Covering and Neighborhood System
3.1.4 Fuzzy Neighborhood System
3.1.5 Neighborhood System and Topological Space
3.1.6 Knowledge Operation in Neighborhood System
3.2 Neighborhood System and Rough Approximations
3.2.1 Neighborhood System Based Rough Sets
3.2.2 Relationship Between Neighborhood System Based Rough Set and VPRS
3.2.3 Neighborhood System Based Rough Approximations in Incomplete Information System
3.3 Reducts Neighborhood Systems
3.3.1 Reducts Neighborhood Systems in Incomplete Information System
3.3.2 Neighborhood Systems Based Approximate Distribution Reducts
3.4 Conclusions
References
Part III Dominance-based Rough Sets and Incomplete Information Systems
Chapter 4 Dominance-based Rough Sets in "," Incomplete Information System
Chapter 5 Dominance-based Rough Sets in "？" Incomplete Information System
Part IV Incomplete Information Systems and Multigranulation Rough Sets
Chapter 6 Multigranulation Rough Sets in Incomplete Information System
