Fuzzy Classifiers Based on Kernel Discriminant Analysis

In this paper, we discuss fuzzy classifiers based on Kernel Discriminant Analysis (KDA) for two-class problems. In our method, first we employ KDA to the given training data and calculate the component that maximally separates two classes in the feature space. Then, in the one-dimensional space obtained by KDA, we generate fuzzy rules with one-dimensional membership functions and tune the slopes and bias terms based on the same training algorithm as that of linear SVMs. Through the computer experiments for two-class problems, we show that the performance of the proposed classifier is comparable to that of SVMs, and we can easily and visually analyze its behavior using the degrees of membership functions.

• Type: Conference
• Year: 2007
• Where: ICANN
• Authors: Ryota Hosokawa, Shigeo Abe
• Keywords: ICANN 2007, Kernel Discriminant Analysis, Neural Networks, One-dimensional Membership Functions, Two-class Problems