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

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, 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