Optimizing Performance Measures for Feature Selection

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
2011 IEEE 11th International Conference on Data Mining (ICDM), 2011, pp. 1170 - 1175
Issue Date:
2011-01
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Feature selection with specific multivariate performance measures is the key to the success of many applications, such as information retrieval and bioinformatics. The existing feature selection methods are usually designed for classification error. In this paper, we present a unified feature selection framework for general loss functions. In particular, we study the novel feature selection paradigm by optimizing multivariate performance measures. The resultant formulation is a challenging problem for high-dimensional data. Hence, a two-layer cutting plane algorithm is proposed to solve this problem, and the convergence is presented. Extensive experiments on largescale and high-dimensional real world datasets show that the proposed method outperforms l1-SVM and SVM-RFE when choosing a small subset of features, and achieves significantly improved performances over SVMperf in terms of F1-score.
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