Learning the Structure of Decision Tree for Generating Fuzzy Rules Based on Genetic Algorithm

Abstract

In orfer to construct the generalized tree structure for the classification samples, the method for learning the structure of the fuzzy decision tree based on Genetic Algorithm(FDTGA) is proposed in this paper. The proposed method has the following features; (1)Genetic Algorithm is applied to the tree structure learning, (2)The structure of the tree is indirectly encoded into the chromosomes, (3)It acquires multiple solutions during the learning. Thus, FDTGA is expected to construct compact and appropriate structures from the complex classification samples defined by the large nember of the attributes.In this paper, after the explanation of the proposing mehtod, we will attempt to acquire the structure of the tree and the fuzzy classification rules for the objective samples from the numerical simulations. Then, the results of the simulations will be compared with the other methods. In the results, we will consider the relation between the acquired rules and the samples, and the effectiveness of the proposed method will be shown.