Turkish Journal of Electrical Engineering and Computer Sciences
Abstract
Maximizing the joint dependency with a minimum size of variables is generally the main task of feature selection. For obtaining a minimal subset, while trying to maximize the joint dependency with the target variable, the redundancy among selected variables must be reduced to a minimum. In this paper, we propose a method based on recently popular minimum Redundancy-Maximum Relevance} (mRMR) criterion. The experimental results show that instead of feeding the features themselves into mRMR, feeding the covariates improves the feature selection capability and provides more expressive variable subsets.
DOI
10.3906/elk-0906-75
Keywords
Mutual information; mRMR; unsupervised learning; support vector machines; SINBAD covariates
First Page
975
Last Page
989
Recommended Citation
KURŞUN, O, ŞAKAR, C. O, FAVOROV, O, AYDIN, N, & GÜRGEN, S. F (2010). Using covariates for improving the minimum redundancy maximum relevance feature selection method. Turkish Journal of Electrical Engineering and Computer Sciences 18 (6): 975-989. https://doi.org/10.3906/elk-0906-75
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Computer Engineering Commons, Computer Sciences Commons, Electrical and Computer Engineering Commons
