Please use this identifier to cite or link to this item: https://doi.org/10.1145/1835804.1835849
Title: Feature selection for support vector regression using probabilistic prediction
Authors: Yang, J.-B.
Ong, C.-J. 
Keywords: Feature ranking
Feature selection
Probabilistic predictions
Random permutation
Support vector regression
Issue Date: 2010
Source: Yang, J.-B.,Ong, C.-J. (2010). Feature selection for support vector regression using probabilistic prediction. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining : 343-351. ScholarBank@NUS Repository. https://doi.org/10.1145/1835804.1835849
Abstract: This paper presents a novel 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 experiment shows that the proposed method generally performs better, and at least as well as the existing methods, with notable advantage when the data set is sparse. © 2010 ACM.
Source Title: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
URI: http://scholarbank.nus.edu.sg/handle/10635/73466
ISBN: 9781450300551
DOI: 10.1145/1835804.1835849
Appears in Collections:Staff Publications

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