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Title: Feature selection using probabilistic prediction of support vector regression
Authors: Yang, J.-B.
Ong, C.-J. 
Keywords: Feature ranking
feature selection
probabilistic predictions
random permutation
support vector regression
Issue Date: Jun-2011
Citation: Yang, J.-B., Ong, C.-J. (2011-06). Feature selection using probabilistic prediction of support vector regression. IEEE Transactions on Neural Networks 22 (6) : 954-962. ScholarBank@NUS Repository.
Abstract: This paper presents a new wrapper-based feature selection method for support vector regression (SVR) using its probabilistic predictions. The method computes the importance of a feature by aggregating the difference, over the feature space, of the conditional density functions of the SVR prediction with and without the feature. As the exact computation of this importance measure is expensive, two approximations are proposed. The effectiveness of the measure using these approximations, in comparison to several other existing feature selection methods for SVR, is evaluated on both artificial and real-world problems. The result of the experiments show that the proposed method generally performs better than, or at least as well as, the existing methods, with notable advantage when the dataset is sparse. © 2011 IEEE.
Source Title: IEEE Transactions on Neural Networks
ISSN: 10459227
DOI: 10.1109/TNN.2011.2128342
Appears in Collections:Staff Publications

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