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.