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E-raamat: Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications

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  • Formaat: EPUB+DRM
  • Ilmumisaeg: 28-Jun-2017
  • Kirjastus: Springer Verlag, Singapore
  • Keel: eng
  • ISBN-13: 9789811049651
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Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and ApplicationsSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 28-Jun-2017
  • Kirjastus: Springer Verlag, Singapore
  • Keel: eng
  • ISBN-13: 9789811049651
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The book will provide:





1) In depth explanation of rough set theory along with examples of the concepts.





2) Detailed discussion on idea of feature selection.





3) Details of various representative and state of the art feature selection techniques along with algorithmic explanations.





4) Critical review of state of the art rough set based feature selection methods covering strength and weaknesses of each.





5) In depth investigation of various application areas using rough set based feature selection.





6) Complete Library of Rough Set APIs along with complexity analysis and detailed manual of using APIs





7) Program files of various representative Feature Selection algorithms along with explanation of each.

The book will be a complete and self-sufficient source both for primary and secondary audience. Starting from basic concepts to state-of-the art implementation, it will be a constant source of help both for practitioners and researchers. Book will provide in-depth explanation of concepts supplemented with working examples to help in practical implementation. As far as practical implementation is concerned, the researcher/practitioner can fully concentrate on his/her own work without any concern towards implementation of basic RST functionality.





Providing complexity analysis along with full working programs will further simplify analysis and comparison of algorithms.
1 Introduction to Feature Selection
1(26)
1.1 Feature
1(2)
1.1.1 Numerical
2(1)
1.1.2 Categorical Attributes
2(1)
1.2 Feature Selection
3(5)
1.2.1 Supervised Feature Selection
4(2)
1.2.2 Unsupervised Feature Selection
6(2)
1.3 Feature Selection Methods
8(3)
1.3.1 Filter Methods
8(2)
1.3.2 Wrapper Methods
10(1)
1.3.3 Embedded Methods
10(1)
1.4 Objective of Feature Selection
11(2)
1.5 Feature Selection Criteria
13(2)
1.5.1 Information Gain
13(1)
1.5.2 Distance
14(1)
1.5.3 Dependency
14(1)
1.5.4 Consistency
14(1)
1.5.5 Classification Accuracy
15(1)
1.6 Feature Generation Schemes
15(3)
1.6.1 Forward Feature Generation
15(1)
1.6.2 Backward Feature Generation
16(1)
1.6.3 Random Feature Generation
17(1)
1.7 Related Concepts
18(4)
1.7.1 Search Organization
18(1)
1.7.2 Generation of a Feature Selection Algorithm
18(1)
1.7.3 Feature Relevance
19(1)
1.7.4 Feature Redundancy
19(1)
1.7.5 Applications of Feature Selection
20(1)
1.7.6 Feature Selection: Issues
21(1)
1.8 Summary
22(5)
Bibliography
23(4)
2 Background
27(26)
2.1 Curse of Dimensionality
27(1)
2.2 Transformation-Based Reduction
28(8)
2.2.1 Linear Methods
29(4)
2.2.2 Nonlinear Methods
33(3)
2.3 Selection-Based Reduction
36(6)
2.3.1 Feature Selection in Supervised Learning
36(1)
2.3.2 Filter Techniques
37(2)
2.3.3 Wrapper Techniques
39(1)
2.3.4 Feature Selection in Unsupervised Learning
40(2)
2.4 Correlation-Based Feature Selection
42(4)
2.4.1 Correlation-Based Measures
43(1)
2.4.2 Correlation-Based Filter Approach (FCBF)
44(2)
2.4.3 Efficient Feature Selection Based on Correlation Measure (ECMBF)
46(1)
2.5 Mutual Information-Based Feature Selection
46(4)
2.5.1 A Mutual Information-Based Feature Selection Method (MIFS-ND)
48(1)
2.5.2 Multi-objective Artificial Bee Colony (MOABC) Approach
48(2)
2.6 Summary
50(3)
Bibliography
50(3)
3 Rough Set Theory
53(28)
3.1 Classical Set Theory
53(3)
3.1.1 Sets
53(1)
3.1.2 Subsets
54(1)
3.1.3 Power Sets
54(1)
3.1.4 Operators
55(1)
3.1.5 Mathematical Symbols for Set Theory
56(1)
3.2 Knowledge Representation and Vagueness
56(2)
3.3 Rough Set Theory (RST)
58(12)
3.3.1 Information Systems
58(1)
3.3.2 Decision Systems
59(1)
3.3.3 Indiscernibility
59(1)
3.3.4 Approximations
60(1)
3.3.5 Positive Region
61(1)
3.3.6 Discernibility Matrix
62(1)
3.3.7 Discernibility Function
63(1)
3.3.8 Decision-Relative Discernibility Matrix
63(3)
3.3.9 Dependency
66(2)
3.3.10 Reducts and Core
68(2)
3.4 Discretization Process
70(2)
3.5 Miscellaneous Concepts
72(1)
3.6 Applications of RST
73(1)
3.7 Summary
73(8)
Bibliography
75(6)
4 Advance Concepts in RST
81(28)
4.1 Fuzzy Set Theory
81(7)
4.1.1 Fuzzy Set
81(2)
4.1.2 Fuzzy Sets and Partial Truth
83(1)
4.1.3 Membership Function
83(1)
4.1.4 Fuzzy Operators
84(2)
4.1.5 Fuzzy Set Representation
86(1)
4.1.6 Fuzzy Rules
86(2)
4.2 Fuzzy-Rough Set Hybridization
88(4)
4.2.1 Supervised Learning and Information Retrieval
89(1)
4.2.2 Feature Selection
89(1)
4.2.3 Rough Fuzzy Set
89(2)
4.2.4 Fuzzy-Rough Set
91(1)
4.3 Dependency Classes
92(10)
4.3.1 Incremental Dependency Classes (IDC)
92(5)
4.3.2 Direct Dependency Classes (DDC)
97(5)
4.4 Redefined Approximations
102(4)
4.4.1 Redefined Lower Approximation
102(2)
4.4.2 Redefined Upper Approximation
104(2)
4.5 Summary
106(3)
Bibliography
106(3)
5 Rough Set-Based Feature Selection Techniques
109(22)
5.1 QuickReduct
109(3)
5.2 Hybrid Feature Selection Algorithm Based on Particle Swarm Optimization (PSO)
112(1)
5.3 Genetic Algorithm
113(2)
5.4 Incremental Feature Selection Algorithm (IFSA)
115(1)
5.5 Feature Selection Method Using Fish Swarm Algorithm (FSA)
116(3)
5.5.1 Representation of Position
117(1)
5.5.2 Distance and Centre of Fish
118(1)
5.5.3 Position Update Strategies
119(1)
5.5.4 Fitness Function
119(1)
5.5.5 Halting Condition
119(1)
5.6 Feature Selection Method Based on QuickReduct and Improved Harmony Search Algorithm (RS-IHS-QR)
119(1)
5.7 A Hybrid Feature Selection Approach Based on Heuristic and Exhaustive Algorithms Using Rough set Theory (FSHEA)
120(3)
5.7.1 Feature Selection Preprocessor
120(2)
5.7.2 Using Relative Dependency Algorithm to Optimize the Selected Features
122(1)
5.8 A Rough Set-Based Feature Selection Approach Using Random Feature Vectors
123(4)
5.9 Summary
127(4)
Bibliography
129(2)
6 Unsupervised Feature Selection Using RST
131(14)
6.1 Unsupervised QuickReduct Algorithm (USQR)
131(3)
6.2 Unsupervised Relative Reduct Algorithm
134(2)
6.3 Unsupervised Fuzzy-Rough Feature Selection
136(1)
6.4 Unsupervised PSO-Based Relative Reduct (US-PSO-RR)
137(3)
6.5 Unsupervised PSO-Based Quick Reduct (US-PSO-QR)
140(2)
6.6 Summary
142(3)
Bibliography
143(2)
7 Critical Analysis of Feature Selection Algorithms
145(10)
7.1 Pros and Cons of Feature Selection Techniques
145(2)
7.1.1 Filter Methods
145(1)
7.1.2 Wrapper Methods
146(1)
7.1.3 Embedded Methods
146(1)
7.2 Comparison Framework
147(1)
7.2.1 Percentage Decrease in Execution Time
147(1)
7.2.2 Memory Usage
147(1)
7.3 Critical Analysis of Various Feature Selection Algorithms
148(5)
7.3.1 QuickReduct
148(1)
7.3.2 Rough Set-Based Genetic Algorithm
149(1)
7.3.3 PSO-QR
150(1)
7.3.4 Incremental Feature Selection Algorithm (IFSA)
151(1)
7.3.5 AFSA
151(1)
7.3.6 Feature Selection Using Exhaustive and Heuristic Approach
152(1)
7.3.7 Feature Selection Using Random Feature Vectors
153(1)
7.4 Summary
153(2)
Bibliography
153(2)
8 RST Source Code
155
8.1 A Simple Tutorial
155(3)
8.1.1 Variable Declaration
156(1)
8.1.2 Array Declaration
156(1)
8.1.3 Comments
156(1)
8.1.4 If-Else Statement
157(1)
8.1.5 Loops
157(1)
8.1.6 Functions
157(1)
8.1.7 LBound and UBound Functions
158(1)
8.2 How to Import the Source Code
158(5)
8.3 Calculating Dependency Using Positive Region
163(10)
8.3.1 Main Function
163(1)
8.3.2 Calculate DRR Function
164(2)
8.3.3 SetDClasses Method
166(1)
8.3.4 FindIndex Function
167(1)
8.3.5 ClrTCC Function
168(1)
8.3.6 AlreadyExists Method
169(1)
8.3.7 InsertObject Method
170(1)
8.3.8 MatchCClasses Function
171(1)
8.3.9 PosReg Function
172(1)
8.4 Calculating Dependency Using Incremental Dependency Classes
173(5)
8.4.1 Main Function
173(1)
8.4.2 CalculateDID Function
173(3)
8.4.3 Insert Method
176(1)
8.4.4 MatchChrom Method
177(1)
8.4.5 MatchDClass Method
178(1)
8.5 Lower Approximation Using Conventional Method
178(5)
8.5.1 Main Method
178(2)
8.5.2 CalculateLAObjects Method
180(1)
8.5.3 FindLAO Method
181(1)
8.5.4 SetDConcept Method
182(1)
8.6 Lower Approximation Using Redefined Preliminaries
183(3)
8.7 Upper Approximation Using Conventional Method
186(1)
8.8 Upper Approximation Using Redefined Preliminaries
187(2)
8.9 QuickReduct Algorithm
189(5)
8.9.1 Miscellaneous Methods
191(1)
8.9.2 Restore Method
192(1)
8.9.3 C_R Method
193(1)
8.10 Summary
194
Dr Summair Raza has PhD specialization in Software Engineering from National University of Science and Technology (NUST), Pakistan. He completed his MS from International Islamic University, Pakistan in 2009. He is also associated with Virtual University of Pakistan as Assistant Professor. He has published various papers in international level journals and conferences. His research interests include Feature Selection, Rough Set Theory, Trend Analysis, Software Architecture, Software Design and Non-Functional Requirements.





Dr Usman Qamar has over 15 years of experience in data engineering both in academia and industry. He has Masters in Computer Systems Design from University of Manchester Institute of Science and Technology (UMIST), UK. His MPhil and PhD in Computer Science are from University of Manchester. Dr Qamars research expertise are in Data and Text Mining, Expert Systems, Knowledge Discovery and Feature Selection. He has published extensively in these subject areas. His Post PhD work at University of Manchester, involved various data engineering projects which included hybrid mechanisms for statistical disclosure and customer profile analysis for shopping with the University of Ghent, Belgium. He is currently an Assistant Professor at Department of Computer Engineering, National University of Sciences and Technology (NUST), Pakistan and also heads the Knowledge and Data Engineering Research Centre (KDRC) at NUST.
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