Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/59617
Title: Bayesian support vector machines for feature ranking and selection
Authors: Chu, W.
Keerthi, S.S.
Ong, C.J. 
Ghahramani, Z.
Issue Date: 2006
Source: Chu, W.,Keerthi, S.S.,Ong, C.J.,Ghahramani, Z. (2006). Bayesian support vector machines for feature ranking and selection. Studies in Fuzziness and Soft Computing 207 : 403-418. ScholarBank@NUS Repository.
Abstract: In this chapter, we develop and evaluate a feature selection algorithm for Bayesian support vector machines. The relevance level of features are represented by ARD (automatic relevance determination) parameters, which are optimized by maximizing the model evidence in the Bayesian framework. The features are ranked in descending order using the optimal ARD values, and then forward selection is carried out to determine the minimal set of relevant features. In the numerical experiments, our approach using ARD for feature ranking can achieve a more compact feature set than standard ranking techniques, along with better generalization performance. © Springer-Verlag Berlin Heidelberg 2006.
Source Title: Studies in Fuzziness and Soft Computing
URI: http://scholarbank.nus.edu.sg/handle/10635/59617
ISSN: 14349922
Appears in Collections:Staff Publications

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