Please use this identifier to cite or link to this item:
|Title:||Feature selection using probabilistic prediction of support vector regression|
support vector regression
|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. https://doi.org/10.1109/TNN.2011.2128342|
|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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Sep 19, 2018
WEB OF SCIENCETM
checked on Sep 4, 2018
checked on Jun 30, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.