Classification and Modeling with Linguistic Information Granules: Advanced Approaches to Linguistic Data Mining

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Springer Science & Business Media, 2004 M11 19 - 308 pages
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Many approaches have already been proposed for classification and modeling in the literature. These approaches are usually based on mathematical mod els. Computer systems can easily handle mathematical models even when they are complicated and nonlinear (e.g., neural networks). On the other hand, it is not always easy for human users to intuitively understand mathe matical models even when they are simple and linear. This is because human information processing is based mainly on linguistic knowledge while com puter systems are designed to handle symbolic and numerical information. A large part of our daily communication is based on words. We learn from various media such as books, newspapers, magazines, TV, and the Inter net through words. We also communicate with others through words. While words play a central role in human information processing, linguistic models are not often used in the fields of classification and modeling. If there is no goal other than the maximization of accuracy in classification and model ing, mathematical models may always be preferred to linguistic models. On the other hand, linguistic models may be chosen if emphasis is placed on interpretability.

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Contents

1 Linguistic Information Granules
1
11 Mathematical Handling of Linguistic Terms
2
12 Linguistic Discretization of Continuous Attributes
4
2 Pattern Classification with Linguistic Rules
11
22 Linguistic Rule Extraction for Classification Problems
12
221 Specification of the Consequent Class
13
222 Specification of the Rule Weight
17
23 Classification of New Patterns by Linguistic Rules
20
823 Other Approaches to Linguistic Rule Generations
166
824 Estimation of Output Values by Linguistic Rules
169
826 Limitations and Extensions
172
827 NonStandard Fuzzy Reasoning Based on the Specificity of Each Linguistic Rule
174
83 Modeling of Nonlinear Fuzzy Functions
177
9 Design of Compact Linguistic Models
181
912 Handling as a SingleObjective Optimization Problem
182
913 Handling as a ThreeObjective Optimization Problem
183

232 VotingBased Method
22
24 Computer Simulations
25
241 Comparison of Four Definitions of Rule Weights
26
242 Simulation Results on Iris Data
29
243 Simulation Results on Wine Data
32
244 Discussions on Simulation Results
35
3 Learning of Linguistic Rules
39
312 Illustration of the Learning Algorithm Using Artificial Test Problems
41
313 Computer Simulations on Iris Data
45
314 Computer Simulations on Wine Data
47
321 Learning Algorithm
48
322 Illustration of the Learning Algorithm Using Artificial Test Problems
50
323 Computer Simulations on Iris Data
54
324 Computer Simulations on Wine Data
56
33 Related Issues
57
332 Adjustment of Membership Functions
62
4 Input Selection and Rule Selection
69
42 Input Selection
70
422 Simulation Results
71
43 Genetic AlgorithmBased Rule Selection
75
431 Basic Idea
76
432 Generation of Candidate Rules
77
433 Genetic Algorithms for Rule Selection
80
434 Computer Simulations
87
44 Some Extensions to Rule Selection
89
441 Heuristics in Genetic Algorithms
90
442 Prescreening of Candidate Rules
93
443 Computer Simulations
96
5 GeneticsBased Machine Learning
103
52 MichiganStyle Algorithm
105
523 Algorithm
107
524 Computer Simulations
108
525 Extensions to the MichiganStyle Algorithm
111
53 PittsburghStyle Algorithm
116
531 Coding of Rule Sets
117
533 Algorithm
119
54 Hybridization of the Two Approaches
121
542 Hybrid Algorithm
124
543 Computer Simulations
125
544 Minimization of the Number of Linguistic Rules
126
6 MultiObjective Design of Linguistic Models
131
62 MultiObjective Genetic Algorithms
134
622 Elitist Strategy
135
63 MultiObjective Rule Selection
136
64 MultiObjective GeneticsBased Machine Learning
139
7 Comparison of Linguistic Discretization with Interval Discretization
143
71 Effects of Linguistic Discretization
144
712 Effect in the Classification Phase
146
713 Summary of Effects of Linguistic Discretization
147
722 Specification of Partially Fuzzified Linguistic Discretization
150
73 Comparison Using Homogeneous Discretization
151
732 Simulation Results on Wine Data
154
74 Comparison Using Inhomogeneous Discretization
155
741 EntropyBased Inhomogeneous Interval Discretization
156
742 Simulation Results on Iris Data
157
743 Simulation Results on Wine Data
158
8 Modeling with Linguistic Rules
161
82 Linguistic Rule Extraction for Modeling Problems
162
821 Linguistic Association Rules for Modeling Problems
163
822 Specification of the Consequent Part
165
92 MultiObjective Rule Selection
185
923 ThreeObjective Genetic Algorithm for Rule Selection
187
924 Simple Numerical Example
189
93 Fuzzy GeneticsBased Machine Learning
190
931 Coding of Rule Sets
192
933 Simple Numerical Example
194
94 Comparison of Two Schemes
196
10 Linguistic Rules with Consequent Real Numbers
199
102 Local Learning of Consequent Real Numbers
201
1022 Incremental Learning Algorithm
203
103 Global Learning
205
1031 Incremental Learning Algorithm
206
1032 Comparison Between Two Learning Schemes
207
104 Effect of the Use of Consequent Real Numbers
208
1042 Simulation Results
210
105 TwinTable Approach
211
1051 Basic Idea
212
1052 Determination of Consequent Linguistic Terms
213
1053 Numerical Example
215
11 Handling of Linguistic Rules in Neural Networks
219
111 Problem Formulation
220
1112 MultiLayer Feedforward Neural Networks
221
112 Handling of Linguistic Rules Using Membership Values
222
1122 Network Architecture
223
113 Handling of Linguistic Rules Using Level Sets
225
1132 Network Architecture
226
114 Handling of Linguistic Rules Using Fuzzy Arithmetic
228
1143 Network Architecture
230
1144 Computer Simulation
233
12 Learning of Neural Networks from Linguistic Rules
235
122 Learning from Linguistic Rules for Classification Problems
237
1223 Extended BackPropagation Algorithm
238
1224 Learning from Linguistic Rules and Numerical Data
241
123 Learning from Linguistic Rules for Modeling Problems
245
1233 Extended BackPropagation Algorithm
246
1234 Learning from Linguistic Rules and Numerical Data
247
13 Linguistic Rule Extraction from Neural Networks
251
131 Neural Networks and Linguistic Rules
252
1321 Basic Idea
253
1323 Computer Simulations
254
133 Linguistic Rule Extraction for Classification Problems
258
1331 Basic Idea
259
1333 Computer Simulations
263
1334 Rule Extraction Algorithm
265
1335 Decreasing the Measurement Cost
267
134 Difficulties and Extensions
270
1341 Scalability to HighDimensional Problems
271
14 Modeling of Fuzzy InputOutput Relations
277
1411 Linear Fuzzy Regression Models
278
1412 Fuzzy RuleBased Systems
280
1413 Fuzzified Takagi Sugeno Models
281
1414 Fuzzifled Neural Networks
283
142 Modeling of Fuzzy Mappings
285
1422 Fuzzy RuleBased Systems
286
1424 Fuzzified Neural Networks
287
1431 Fuzzy Classification of NonFuzzy Patterns
288
1432 Fuzzy Classification of Interval Patterns
291
1434 Effect of Fuzziflcation of Input Patterns
292
Index
305
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