SCLS: Multi-label feature selection based on scalable criterion for large label set. (June 2017) - Lee, Jaesung; Kim, Dae-Won
Abstract: Multi-label feature selection involves the selection of relevant features from multi-labeled datasets, resulting in a potential improvement of multi-label learning accuracy. In conventional multi-label feature selection methods, the final feature subset is obtained by identifying the features of high relevance with low redundancy. Thus, accurate score evaluation is a key factor for obtaining an effective feature subset. However, conventional methods suffer from inaccurate conditional relevance evaluation when a large number of labels are involved. As a result, irrelevant features can be a member of the final feature subset, leading to low multi-label learning accuracy. In this paper, we propose a new multi-label feature selection method. Using a scalable relevance evaluation process that evaluates conditional relevance more accurately, the proposed method significantly improves multi-label learning accuracy compared with conventional multi-label feature selection methods. Abstract : Highlights: A multi-label feature selection method for multi-label classification is proposed. We propose a new scalable relevance evaluation process for feature evaluation. The proposed method is designed to use a simpler dependency calculation process. An effective approximation for the relevance evaluation is devised.