Dynamic mutual information-based feature selection for multi-label learning

Article type: Research Article

Authors: Kim, Kyung-Jun | Jun, Chi-Hyuck^*

Affiliations: Department of Industrial and Management Engineering, Pohang University of Science & Technology (POSTECH), Pohang, Korea

Correspondence: [*] Corresponding author: Chi-Hyuck Jun, Department of Industrial and Management Engineering, Pohang University of Science & Technology (POSTECH), Pohang, Korea. E-mail: [email protected].

Abstract: In classification problems, feature selection is used to identify important input features to reduce the dimensionality of the input space while improving or maintaining classification performance. Traditional feature selection algorithms are designed to handle single-label learning, but classification problems have recently emerged in multi-label domain. In this study, we propose a novel feature selection algorithm for classifying multi-label data. This proposed method is based on dynamic mutual information, which can handle redundancy among features controlling the input space. We compare the proposed method with some existing problem transformation and algorithm adaptation methods applied to real multi-label datasets using the metrics of multi-label accuracy and hamming loss. The results show that the proposed method demonstrates more stable and better performance for nearly all multi-label datasets.

Keywords: Feature selection, multi-label learning, dynamic mutual information, filter ranking, algorithm adaptation

DOI: 10.3233/IDA-226666

Journal: Intelligent Data Analysis, vol. 27, no. 4, pp. 891-909, 2023