ABSTRACT. Feature selection with specific multivariate performance measures is the key to the success of many applications such as information retrieval. In this paper, we propose a feature selection method for multivariate performance measures. The proposed method forms an optimization problem with exponential size of both feature groups and label configurations for a given dataset. To address this problem, a two-layer cutting plane algorithm is proposed. The outer layer performs group feature generation; while the inner layer learns the label configuration for multivariate performance measures. Comprehensive experiments on large-scale and high-dimensional real world datasets show that the proposed method can significantly outperform l 1 -SVM and SVM-RFE when choosing a small subset of features, and achieve significantly improved performances over SVM perf in terms of F 1 -score. It also learns a sparse yet effective decision rule for multivariate performance measures.
• Mao and Tsang REF proposed a generalized sparse regularizer for multivariate performance measure optimization.
A Feature Selection Method for Multivariate Performance Measures
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012
Computer Science Medicine