Classification and modeling with linguistic information granules : advanced approaches advanced approaches to linguistic data mining
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
Classification
1 online resource (xi, 307 pages) : illustrations
9783540268758, 9783540207672, 9786610462513, 3540268758, 3540207678, 6610462518
262677773
Cover
Table of Contents
1. Linguistic Information Granules
1.1 Mathematical Handling of Linguistic Terms
1.2 Linguistic Discretization of Continuous Attributes
2. Pattern Classification with Linguistic Rules
2.1 Problem Description
2.2 Linguistic Rule Extraction for Classification Problems
2.3 Classification of New Patterns by Linguistic Rules
2.4 Computer Simulations
3. Learning of Linguistic Rules
3.1 Reward-Punishment Learning
3.2 Analytical Learning
3.3 Related Issues
4. Input Selection and Rule Selection
4.1 Curse of Dimensionality
4.2 Input Selection
4.3 Genetic Algorithm-Based Rule Selection
4.4 Some Extensions to Rule Selection
5. Genetics-Based Machine Learning
5.1 Two Approaches in Genetics-Based Machine Learning
5.2 Michigan-Style Algorithm
5.3 Pittsburgh-Style Algorithm
5.4 Hybridization of the Two Approaches
6. Multi-Objective Design of Linguistic Models
6.1 Formulation of Three-Objective Problem
6.2 Multi-Objective Genetic Algorithms
6.3 Multi-Objective Rule Selection
6.4 Multi-Objective Genetics-Based Machine Learning
7. Comparison of Linguistic Discretization with Interval Discretization
7.1 Effects of Linguistic Discretization
7.2 Specification of Linguistic Discretization from Interval Discretization
7.3 Comparison Using Homogeneous Discretization
7.4 Comparison Using Inhomogeneous Discretization
8. Modeling with Linguistic Rules
8.1 Problem Description
8.2 Linguistic Rule Extraction for Modeling Problems
8.3 Modeling of Nonlinear Fuzzy Functions
9. Design of Compact Linguistic Models
9.1 Single-Objective and Multi-Objective Formulations
9.2 Multi-Objective Rule Selection
9.3 Fuzzy Genetics-Based Machine Learning
9.4 Comparison of Two Schemes
10. Linguistic Rules with Consequent Real Numbers
10.1 Consequent Real Numbers
10.2 Local Learning of Consequent Real Numbers
10.3 Global Learning
10.4 Effect of the Use of Consequent Real Numbers
10.5 Twin-Table Approach
11. Handling of Linguistic Rules in Neural Networks
11.1 Problem Formulation
11.2 Handling of Linguistic Rules Using Membership Values
11.3 Handling of Linguistic Rules Using Level Sets
11.4 Handling of Linguistic Rules Using Fuzzy Arithmetic
12. Learning of Neural Networks from Linguistic Rules
12.1 Back-Propagation Algorithm
12.2 Learning from Linguistic Rules for Classification Problems
12.3 Learning from Linguistic Rules for Modeling Problems
13. Linguistic Rule Extraction from Neural Networks
13.1 Neural Networks and Linguistic Rules
13.2 Linguistic Rule Extraction for Modeling Problems
13.3 Linguistic Rule Extraction for Classification Problems
13.4 Difficulties and Extensions
14. Modeling of Fuzzy Input-Output Relations
14.1 Modeling of Fuzzy Number-Valued Functions
14.2 Modeling of Fuzzy Mappings
14.3 Fuzzy Classification