Volume 7, Issue 1, January 2014, Pages 12 - 24
A DC programming approach for feature selection in the Minimax Probability Machine
Liming Yang, Ribo Ju
Available Online 1 January 2014.
- https://doi.org/10.1080/18756891.2013.864471How to use a DOI?
- Feature selection, Minimax probability machine, DC programming
- This paper presents a new feature selection framework based on the -norm, in which data are summarized by their moments of the class conditional densities. However, discontinuity of the -norm makes it difficult to find the optimal solution. We apply a proper approximation of the -norm and a bound on the misclassification probability involving the mean and covariance of the dataset, to derive a robust difference of convex functions (DC) program formulation, while the DC optimization algorithm is used to solve the problem effectively. Furthermore, a kernelized version of this problem is also presented in this work. Experimental results on both real and synthetic datasets show that the proposed formulations can select fewer features than the traditional Minimax Probability Machine and the -norm state.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - JOUR AU - Liming Yang AU - Ribo Ju PY - 2014 DA - 2014/01 TI - A DC programming approach for feature selection in the Minimax Probability Machine JO - International Journal of Computational Intelligence Systems SP - 12 EP - 24 VL - 7 IS - 1 SN - 1875-6883 UR - https://doi.org/10.1080/18756891.2013.864471 DO - https://doi.org/10.1080/18756891.2013.864471 ID - Yang2014 ER -