Classification and Modeling with Linguistic Information Granules

1. Front Matter

   Pages I-XI

   PDF

2. Linguistic Information Granules

   Pages 1-9

3. Pattern Classification with Linguistic Rules

   Pages 11-37

4. Learning of Linguistic Rules

   Pages 39-67

5. Input Selection and Rule Selection

   Pages 69-101

6. Genetics-Based Machine Learning

   Pages 103-129

7. Multi-Objective Design of Linguistic Models

   Pages 131-141

8. Comparison of Linguistic Discretization with Interval Discretization

   Pages 143-160

9. Modeling with Linguistic Rules

   Pages 161-179

10. Design of Compact Linguistic Models

    Pages 181-198

11. Linguistic Rules with Consequent Real Numbers

    Pages 199-217

12. Handling of Linguistic Rules in Neural Networks

    Pages 219-234

13. Learning of Neural Networks from Linguistic Rules

    Pages 235-250

14. Linguistic Rule Extraction from Neural Networks

    Pages 251-275

15. Modeling of Fuzzy Input—Output Relations

    Pages 277-293

16. Back Matter

    Pages 295-307

    PDF

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.

• algorithms
• computational intelligence
• data mining
• fuzzy logic
• genetic algorithms
• linguistic modelling
• linguistic rules
• machine learning
• neural networks
• rough sets
• soft computing

• Department of Computer Science and Intelligent Systems, Osaka Prefecture University, Sakai Osaka, Japan

  Hisao Ishibuchi, Tomoharu Nakashima

• Department of Electrical Engineering and Computer Sciences, Graduate School of Engineering, University of Hyogo, Himeji Hyogo, Japan

  Manabu Nii

Policies and ethics