Using covariates for improving the minimum redundancy maximum relevance feature selection method

Authors

OLCAY KURŞUN
CEMAL OKAN ŞAKAR
OLEG FAVOROV
NİZAMETTİN AYDIN
SADIK FİKRET GÜRGEN

DOI

10.3906/elk-0906-75

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.

Keywords

Mutual information; mRMR; unsupervised learning; support vector machines; SINBAD covariates

First Page

975

Last Page

989

Recommended Citation

KURŞUN, OLCAY; ŞAKAR, CEMAL OKAN; FAVOROV, OLEG; AYDIN, NİZAMETTİN; and GÜRGEN, SADIK FİKRET (2010) "Using covariates for improving the minimum redundancy maximum relevance feature selection method," Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 18: No. 6, Article 4. https://doi.org/10.3906/elk-0906-75
Available at: https://journals.tubitak.gov.tr/elektrik/vol18/iss6/4