Margin attribute reductions for multi-label classification

Multi-label classification is a typical supervised machine learning problem and widely applied in text classification and image recognition. When there are redundant attributes in the data, the efficiency of classification will be reduced. However, the existing attribute reduction algorithms have high computational complexity. This paper aims to design an efficient attribute reduction algorithm. The k pairs of boundary samples were selected from the positive and negative classes respectively, and the distance between each pair was calculated as the evaluation of attributes. By maximizing the evaluation function, the definition of reduction and the design of the algorithm were established. The comparison experiment is carried out on eight generic multi-label data. The experimental results show that the attribute importance evaluation defined in this paper can better represent the classification performance of the attribute for multi-label classification. The boundary samples can better reflect the classification effect of attributes. The proposed model avoids the point-by-point statistics of all samples' information and improves the computational efficiency.