Computational intelligence and feature selection: rough and fuzzy approaches
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Oxford
Wiley-Blackwell
2008
|
| Schriftenreihe: | IEEE Press series on computational intelligence
|
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016742265&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | XV, 339 S. Ill., graph. Darst. |
| ISBN: | 9780470229750 0470229756 |
Internformat
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| 100 | 1 | |a Jensen, Richard |d 1949- |e Verfasser |0 (DE-588)129500380 |4 aut | |
| 245 | 1 | 0 | |a Computational intelligence and feature selection |b rough and fuzzy approaches |c by Richard Jensen, Qiang Shen |
| 264 | 1 | |a Oxford |b Wiley-Blackwell |c 2008 | |
| 300 | |a XV, 339 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a IEEE Press series on computational intelligence | |
| 650 | 4 | |a Artificial intelligence / Mathematical models | |
| 650 | 4 | |a Set theory | |
| 650 | 4 | |a Künstliche Intelligenz | |
| 650 | 4 | |a Mathematisches Modell | |
| 650 | 4 | |a Artificial intelligence |x Mathematical models | |
| 650 | 4 | |a Set theory | |
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Datensatz im Suchindex
| DE-BY-TUM_call_number | 0002 DAT 708f 2010 A 4916 |
|---|---|
| DE-BY-TUM_katkey | 1733271 |
| DE-BY-TUM_location | 00 |
| DE-BY-TUM_media_number | 040090427433 |
| _version_ | 1821932679688355840 |
| adam_text | CONTENTS
PREFACE
xiii
1
THE IMPORTANCE OF FEATURE SELECTION
1
1.1.
Knowledge Discovery
/ 1
1.2.
Feature Selection
/ 3
1.2.1.
The Task
/ 3
1.2.2.
The Benefits
/ 4
1.3.
Rough Sets
/ 4
1.4.
Applications
/ 5
1.5.
Structure
/ 7
2
SET THEORY
13
2.1.
Classical Set Theory
/ 13
2.1.1.
Definition
/ 13
2.1.2.
Subsets
/ 14
2.1.3.
Operators
/ 14
2.2.
Fuzzy Set Theory
/ 15
2.2.1.
Definition
/ 16
2.2.2.
Operators
/ 17
2.2.3.
Simple Example
/ 19
2.2.4.
Fuzzy Relations and Composition
/ 20
2.2.5.
Approximate Reasoning
/ 22
V¡
CONTENTS
2.2.6.
Linguistic Hedges
/ 24
2.2.7.
Fuzzy Sets and Probability
/ 25
2.3.
Rough Set Theory
/ 25
2.3.1.
Information and Decision Systems
/ 26
2.3.2.
Indiscernibility
/ 27
2.3.3.
Lower and Upper Approximations
/ 28
2.3.4.
Positive, Negative, and Boundary Regions
/ 28
2.3.5.
Feature Dependency and Significance
/ 29
2.3.6.
Reducts
/ 30
2.3.7.
Discernibility Matrix
/ 31
2.4.
Fuzzy-Rough Set Theory
/ 32
2.4.1.
Fuzzy Equivalence Classes
/ 33
2.4.2.
Fuzzy-Rough Sets
/ 34
2.4.3.
Rough-Fuzzy Sets
/ 35
2.4.4.
Fuzzy-Rough Hybrids
/ 35
2.5.
Summary
/ 37
3
CLASSIFICATION METHODS
39
3.1.
Crisp Approaches
/ 40
3.1.1.
Rule
Inducere
/ 40
3.1.2.
Decision Trees
/ 42
3.1.3.
Clustering
/ 42
3.1.4.
Naive
Bayes
/ 44
3.1.5.
Inductive Logic Programming
/ 45
3.2.
Fuzzy Approaches
/ 45
3.2.1.
Lozowski s Method
/ 46
3.2.2.
Subsethood-Based Methods
/ 48
3.2.3.
Fuzzy Decision Trees
/ 53
3.2.4.
Evolutionary Approaches
/ 54
3.3.
Rulebase Optimization
/ 57
3.3.1.
Fuzzy Interpolation
/ 57
3.3.2.
Fuzzy Rule Optimization
/ 58
3.4.
Summary
/ 60
4
DIMENSIONALITY REDUCTION
61
4.1.
Transformation-Based Reduction
/ 63
4.1.1.
Linear Methods
/ 63
4.1.2.
Nonlinear Methods
/ 65
4.2.
Selection-Based Reduction
/ 66
CONTENTS
VU
4.2.1. Filter
Methods
/ 69
4.2.2.
Wrapper Methods
/ 78
4.2.3.
Genetic Approaches
/ 80
4.2.4.
Simulated Annealing Based Feature Selection
/81
4.3.
Summary
/ 83
5
ROUGH SET BASED APPROACHES TO FEATURE
SELECTION
85
5.1.
Rough Set Attribute Reduction
/ 86
5.1.1.
Additional Search Strategies
/ 89
5.1.2.
Proof of QuickReduct
Monotonicity
/ 90
5.2.
RSAR Optimizations
/ 91
5.2.1.
Implementation Goals
/ 91
5.2.2.
Implementational Optimizations
/ 91
5.3.
Discemibility Matrix Based Approaches
/ 95
5.3.1.
Johnson Reducer
/ 95
5.3.2.
Compressibility Algorithm
/ 96
5.4.
Reduction with Variable Precision Rough Sets
/ 98
5.5.
Dynamic Reducts
/ 100
5.6.
Relative Dependency Method
/ 102
5.7.
Tolerance-Based Method
/ 103
5.7.1.
Similarity Measures
/ 103
5.7.2.
Approximations and Dependency
/ 104
5.8.
Combined Heuristic Method
/ 105
5.9.
Alternative Approaches
/ 106
5.10.
Comparison of Crisp Approaches
/ 106
5.10.1.
Dependency Degree Based Approaches
/ 107
5.10.2.
Discemibility Matrix Based Approaches
/ 108
5.11.
Summary /111
6
APPLICATIONS I: USE OF RSAR
113
6.1.
Medical Image Classification
/113
6.1.1.
Problem Case
/114
6.1.2.
Neural Network Modeling
/115
6.1.3.
Results
/116
6.2.
Text Categorization
/ 117
6.2.1.
Problem Case
/117
6.2.2.
Metrics
/118
6.2.3.
Datasets
Used
/118
Viii CONTENTS
6.2.4.
Dimensionality Reduction
/119
6.2.5.
Information Content of Rough Set Reducts
/ 120
6.2.6.
Comparative Study of TC Methodologies
/ 121
6.2.7.
Efficiency Considerations of RSAR
/ 124
6.2.8.
Generalization
/ 125
6.3.
Algae Estimation
/ 126
6.3.1.
Problem Case
/ 126
6.3.2.
Results
/ 127
6.4.
Other Applications
/ 128
6.4.1.
Prediction of Business Failure
/ 128
6.4.2.
Financial Investment
/ 129
6.4.3.
Bioinformatics and Medicine
/129
6.4.4.
Fault Diagnosis
/ 130
6.4.5.
Spacial and Meteorological Pattern
Classification
/ 131
6.4.6.
Music and Acoustics
/ 131
6.5.
Summary
/ 132
7
ROUGH AND FUZZY HYBRIDIZATION
133
7.1.
Introduction
/ 133
7.2.
Theoretical Hybridization
/ 134
7.3.
Supervised Learning and Information Retrieval
/ 136
7.4.
Feature Selection
/ 137
7.5.
Unsupervised Learning and Clustering
/138
7.6.
Neurocomputing
/139
7.7.
Evolutionary and Genetic Algorithms
/ 140
7.8.
Summary
/ 141
8
FUZZY-ROUGH FEATURE SELECTION
143
8.1.
Feature Selection with Fuzzy-Rough Sets
/ 144
8.2.
Fuzzy-Rough Reduction Process
/ 144
8.3.
Fuzzy-Rough QuickReduct
/ 146
8.4.
Complexity Analysis
/ 147
8.5.
Worked Examples
/ 147
8.5.1.
Crisp Decisions
/ 148
8.5.2.
Fuzzy Decisions
/ 152
8.6.
Optimizations
/ 153
8.7.
Evaluating the Fuzzy-Rough Metric
/ 154
8.7.1.
Compared Metrics
/ 155
CONTENTS
¡X
8.7.2.
Metric
Comparison
/ 157
8.7.3.
Application to Financial Data
/ 159
8.8.
Summary
/161
9
NEW DEVELOPMENTS OF FRFS
163
9.1.
Introduction
/ 163
9.2.
New Fuzzy-Rough Feature Selection
/ 164
9.2.1.
Fuzzy Lower Approximation Based FS
/ 164
9.2.2.
Fuzzy Boundary Region Based FS
/ 168
9.2.3.
Fuzzy-Rough Reduction with Fuzzy Entropy
/ 171
9.2.4.
Fuzzy-Rough Reduction with Fuzzy Gain
Ratio
/ 173
9.2.5.
Fuzzy Discernibility Matrix Based FS
/ 174
9.2.6.
Vaguely Quantified Rough Sets (VQRS)
/ 178
9.3.
Experimentation
/ 180
9.3.1.
Experimental Setup
/ 180
9.3.2.
Experimental Results
/ 180
9.3.3.
Fuzzy Entropy Experimentation
/ 182
9.4.
Proofs
/ 184
9.5.
Summary
/ 190
10
FURTHER ADVANCED FS METHODS
191
1
0.1.
Feature Grouping
/191
Fuzzy Dependency
/ 192
Scaled Dependency
/ 192
The Feature Grouping Algorithm
/ 193
Selection Strategies
/ 194
Algorithmic Complexity
/ 195
10.2.
Ant Colony Optimization-Based Selection
/ 195
10.2.1.
Ant Colony Optimization
/ 196
10.2.2.
Traveling Salesman Problem
/ 197
10.2.3.
Ant-Based Feature Selection
/ 197
10.3.
Summary
/ 200
11
APPLICATIONS II: WEB CONTENT CATEGORIZATION
203
1
1.1.
Text Categorization
/ 203
11.1.1.
Rule-Based Classification
/ 204
11.1.2.
Vector-Based Classification
/ 204
11.1.3.
Latent Semantic Indexing
/ 205
10.
.1
10.
.2.
10.
.3.
10.
.4.
10.
.5.
X
CONTENTS
11.1.4.
Probabilistic
/ 205
11.1.5.
Term Reduction
/ 206
11.2.
System Overview
/ 207
11.3.
Bookmark Classification
/ 208
11.3.1.
Existing Systems
/ 209
11.3.2.
Overview
/ 210
11.3.3.
Results
/212
11.4.
Web Site Classification
/ 214
11.4.1.
Existing Systems
/ 214
11.4.2.
Overview
/215
11.4.3.
Results
/215
11.5.
Summary
/218
12
APPLICATIONS III: COMPLEX SYSTEMS MONITORING
219
1
2.1.
The Application
/ 221
12.1.1.
Problem Case
/ 221
12.1.2.
Monitoring System
/ 221
12.2.
Experimental Results
/ 223
12.2.1.
Comparison with Unreduced Features
/ 223
M.I.I. Comparison with Entropy-Based Feature
Selection
/ 226
12.2.3.
Comparison with PCA and Random Reduction
/ 227
12.2.4.
Alternative Fuzzy Rule Inducer
/ 230
12.2.5.
Results with Feature Grouping
/ 231
12.2.6.
Results with Ant-Based FRFS
/ 233
12.3.
Summary
/ 236
13
APPLICATIONS IV: ALGAE POPULATION ESTIMATION
237
13.1.
Application Domain
/ 238
13.1.1.
Domain Description
/ 238
13.1.2.
Predictors
/ 240
13.2.
Experimentation
/ 241
13.2.1.
Impact of Feature Selection
/ 241
13.2.2.
Comparison with Relief
/ 244
13.2.3.
Comparison with Existing Work
/ 248
13.3.
Summary
/ 248
14
APPLICATIONS V: FORENSIC GLASS ANALYSIS
259
1
4.1.
Background
/ 259
CONTENTS
ХІ
14.2.
Estimation
of Likelihood Ratio
/ 261
14.2.1.
Exponential Model
/ 262
14.2.2.
Biweight Kernel Estimation
/ 263
14.2.3.
Likelihood Ratio with Biweight and Boundary
Kernels
/ 264
14.2.4.
Adaptive Kernel
/ 266
14.3.
Application
/ 268
14.3.1.
Fragment Elemental Analysis
/ 268
14.3.2.
Data Preparation
/ 270
14.3.3.
Feature Selection
/ 270
14.3.4.
Estimators
/ 270
14.4.
Experimentation
/ 270
14.4.1.
Feature Evaluation
/ 272
14.4.2.
Likelihood Ratio Estimation
/ 272
14.5.
Glass Classification
/ 274
14.6.
Summary
/ 276
15
SUPPLEMENTARY DEVELOPMENTS AND
INVESTIGATIONS
279
1
5.1.
RSAR-SAT
/ 279
15.1.1.
Finding Rough Set Reducts
/ 280
15.1.2.
Preprocessing Clauses
/ 281
15.1.3.
Evaluation
/ 282
15.2.
Fuzzy-Rough Decision Trees
/ 283
15.2.1.
Explanation
/ 283
15.2.2.
Experimentation
/ 284
15.3.
Fuzzy-Rough Rule Induction
/ 286
15.4.
Hybrid Rule Induction
/ 287
15.4.1.
Hybrid Approach
/ 288
15.4.2.
Rule Search
/ 289
15.4.3.
Walkthrough
/ 291
15.4.4.
Experimentation
/ 293
15.5.
Fuzzy Universal Reducts
/ 297
15.6.
Fuzzy-Rough Clustering
/ 298
15.6.1.
Fuzzy-Rough
с
-Means /
298
15.6.2.
General Fuzzy-Rough Clustering
/ 299
15.7.
Fuzzification Optimization
/ 299
15.8.
Summary
/ 300
Xli
CONTENTS
APPENDIX A
METRIC COMPARISON RESULTS: CLASSIFICATION
DATASETS
301
APPENDIX
В
METRIC COMPARISON RESULTS: REGRESSION
DATASETS
309
REFERENCES
313
INDEX
337
|
| any_adam_object | 1 |
| author | Jensen, Richard 1949- |
| author_GND | (DE-588)129500380 |
| author_facet | Jensen, Richard 1949- |
| author_role | aut |
| author_sort | Jensen, Richard 1949- |
| author_variant | r j rj |
| building | Verbundindex |
| bvnumber | BV035073910 |
| callnumber-first | Q - Science |
| callnumber-label | Q335 |
| callnumber-raw | Q335 |
| callnumber-search | Q335 |
| callnumber-sort | Q 3335 |
| callnumber-subject | Q - General Science |
| classification_rvk | ST 301 |
| classification_tum | DAT 708f DAT 773f |
| ctrlnum | (OCoLC)176924585 (DE-599)BVBBV035073910 |
| dewey-full | 006.30151132 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.30151132 |
| dewey-search | 006.30151132 |
| dewey-sort | 16.30151132 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
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| id | DE-604.BV035073910 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T13:19:31Z |
| institution | BVB |
| isbn | 9780470229750 0470229756 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016742265 |
| oclc_num | 176924585 |
| open_access_boolean | |
| owner | DE-703 DE-473 DE-BY-UBG DE-91 DE-BY-TUM |
| owner_facet | DE-703 DE-473 DE-BY-UBG DE-91 DE-BY-TUM |
| physical | XV, 339 S. Ill., graph. Darst. |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | Wiley-Blackwell |
| record_format | marc |
| series2 | IEEE Press series on computational intelligence |
| spellingShingle | Jensen, Richard 1949- Computational intelligence and feature selection rough and fuzzy approaches Artificial intelligence / Mathematical models Set theory Künstliche Intelligenz Mathematisches Modell Artificial intelligence Mathematical models Künstliche Intelligenz (DE-588)4033447-8 gnd Fuzzy-Menge (DE-588)4061868-7 gnd Grobmenge (DE-588)4362502-2 gnd Merkmalsextraktion (DE-588)4314440-8 gnd |
| subject_GND | (DE-588)4033447-8 (DE-588)4061868-7 (DE-588)4362502-2 (DE-588)4314440-8 |
| title | Computational intelligence and feature selection rough and fuzzy approaches |
| title_auth | Computational intelligence and feature selection rough and fuzzy approaches |
| title_exact_search | Computational intelligence and feature selection rough and fuzzy approaches |
| title_full | Computational intelligence and feature selection rough and fuzzy approaches by Richard Jensen, Qiang Shen |
| title_fullStr | Computational intelligence and feature selection rough and fuzzy approaches by Richard Jensen, Qiang Shen |
| title_full_unstemmed | Computational intelligence and feature selection rough and fuzzy approaches by Richard Jensen, Qiang Shen |
| title_short | Computational intelligence and feature selection |
| title_sort | computational intelligence and feature selection rough and fuzzy approaches |
| title_sub | rough and fuzzy approaches |
| topic | Artificial intelligence / Mathematical models Set theory Künstliche Intelligenz Mathematisches Modell Artificial intelligence Mathematical models Künstliche Intelligenz (DE-588)4033447-8 gnd Fuzzy-Menge (DE-588)4061868-7 gnd Grobmenge (DE-588)4362502-2 gnd Merkmalsextraktion (DE-588)4314440-8 gnd |
| topic_facet | Artificial intelligence / Mathematical models Set theory Künstliche Intelligenz Mathematisches Modell Artificial intelligence Mathematical models Fuzzy-Menge Grobmenge Merkmalsextraktion |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016742265&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT jensenrichard computationalintelligenceandfeatureselectionroughandfuzzyapproaches AT shenqiang computationalintelligenceandfeatureselectionroughandfuzzyapproaches |
Inhaltsverzeichnis
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Teilbibliothek Stammgelände
| Signatur: |
0002 DAT 708f 2010 A 4916 Lageplan |
|---|---|
| Exemplar 1 | Ausleihbar Am Standort |