A novel multi-label feature selection method based on knowledge consistency-independence index

Abstract

Multi-label classification encounters the challenge of dealing with high dimensional data. In response to this challenge, numerous researchers have proposed various multi-label feature selection methods from different perspectives. However, existing methods overlook the consistency and independence of knowledge granules, and thus fail to extract valuable and distinctive information from the knowledge granules that is relevant to the label space. To address this issue, we propose a novel multi-label feature selection method based on the knowledge consistency-independence index (CIMLFS). Firstly, we introduce the concepts of knowledge consistency granularity and knowledge independence granularity to explore valuable and distinctive information from the knowledge granule families. Secondly, based upon these concepts, we define the consistency coefficient, independence coefficient, and consistency gain for features, ultimately considering the three perspectives to achieve the knowledge consistency-independence index. Furthermore, we present a multi-label feature selection method utilizing the index. Finally, to assess the effectiveness of CIMLFS, we conduct comparative experiments with eight representative multi-label feature selection methods on twelve benchmark multi-label data sets and using four evaluation metrics. The final experimental results indicate that CIMLFS ranks the first on three metrics and the second on one metric.