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Novel multi-class feature selection methods using sensitivity analysis of posterior probabilities

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4 Author(s)
Kai-Quan Shen ; Dept. of Mech. Eng., Nat. Univ. of Singapore, Singapore ; Chong-Jin Ong ; Xiao-Ping Li ; Wilder-Smith, E.

Novel feature-selection methods are proposed for multi-class support-vector-machine (SVM) learning. They are based on two new feature-ranking criteria. Both criteria, collectively termed multi-class feature-based sensitivity of posterior probabilities (MFSPP), evaluate the importance of a feature by computing the aggregate value, over the feature space, of the absolute difference of the probabilistic outputs of the multi-class SVM with and without the feature. In their original form, the criteria are computationally expensive and three approximations, MFSPP1-MFSPP3, are then proposed. In a carefully controlled experimental study, all these three approximations are tested on various artificial and benchmark datasets. Results show that they outperform the multi-class versions of support-vector-machine recursive feature-elimination method (SVM-RFE) and other standard filtering methods, with one of the three proposed approximations having a slight edge over the other two. Based on the experiments, the advantage of the proposed methods is particularly significant when training dataset is sparse.

Published in:

Systems, Man and Cybernetics, 2008. SMC 2008. IEEE International Conference on

Date of Conference:

12-15 Oct. 2008