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

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  • マルチラベル量質混在データを対象とした適応共鳴理論に基づくクラスタリング手法による識別器の改良

Abstract

<p>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.</p>

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