Classification and Modeling with Linguistic Information Granules: Advanced Approaches to Linguistic Data Mining (Advanced Information Processing) - Hardcover

Reseña del editor

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.

Reseña del editor

Whereas computer systems can easily handle even complicated and nonlinear mathematical models, human information processing is mainly based on linguistic knowledge. So the main advantage of using linguistic terms even with vague ranges is the intuitive interpretability of linguistic rules.

Ishibuchi and his coauthors explain how classification and modeling can be handled in a human-understandable manner. They design a framework that can extract linguistic knowledge from numerical data by first identifying linguistic terms, then combining these terms into linguistic rules, and finally constructing a rule set from these linguistic rules. They combine their approach with state-of-the-art soft computing techniques such as multi-objective genetic algorithms, genetics-based machine learning, and fuzzified neural networks. Finally they demonstrate the usability of the combined techniques with various simulation results.

In this largely self-contained volume, students specializing in soft computing will appreciate the detailed presentation, carefully discussed algorithms, and the many simulation experiments, while researchers will find a wealth of new design schemes, thorough analysis, and inspiring new research.