Classifier Design Based on Class-Wise Fast Topological CIM-Based Adaptive Resonance Theory

Abstract

Clustering algorithms can flexibly extract useful knowledge from input data. Therefore, clustering algorithms are often applied to data preprocessing such as dimensionality reduction and feature extraction. Clustering algorithms can also be applied to classifiers thanks to a good knowledge extraction ability. As a conventional study of applying clustering algorithms to classifier design, the algorithm has been proposed that explicitly learns decision boundaries by applying a clustering algorithm to each class of data. However, there are some problems such as instability of learning and slow processing. In this study, we propose a classifier by utilizing the Fast Topological CIM-based Adaptive Resonance Theory (FTCA) that achieves both excellent self-organization performance and high-speed learning. Our experimental results in this paper show that the proposed algorithm has better classification performance compared to other clustering-based classifiers.