Improvement of a Classifier Using Adaptive Resonance Theory-Based Clustering for Multi-Label Mixed Data

Abstract

Various multi-label classifiers have been proposed for multi-label classification problems. Our previous study has proposed an adaptive resonance theory (ART)-based clustering method using correntropy-induced metric (CIM) as a similarity measure, called CIM-based ART for Multi-Label Mixed Data (CA-MLMD). CA-MLMD adaptively and continually generates nodes corresponding to input data, and the generated nodes are used as a classifier. Moreover, CA-MLMD learns new data and label information continually and handles mixed datasets that contain both numerical and categorical attributes. However, CA-MLMD is highly affected by local data points around the node in learning categorical attributes, which may deteriorate classification performance. This study proposes CA-MLMD-weight (CA-MLMD-w), which uses weights defined by categorical attributes of each node and reduces effects of local data points by considering categorical attributes of the entire data. Numerical experiments on real-world datasets show the effectiveness of the proposed method.