Conditional mutual information based feature selection for classification task

Authors:
Jana Novovičová;Petr Somol;Michal Haindl;Pavel Pudil
Affiliations:
Dept. of Pattern Recognition, Institute of Academy of Sciences of the Czech Republic and Faculty of Management, Prague University of Economics, Czech Republic;Dept. of Pattern Recognition, Institute of Academy of Sciences of the Czech Republic and Faculty of Management, Prague University of Economics, Czech Republic;Dept. of Pattern Recognition, Institute of Academy of Sciences of the Czech Republic and Faculty of Management, Prague University of Economics, Czech Republic;Faculty of Management, Prague University of Economics, Czech Republic and Dept. of Pattern Recognition, Institute of Academy of Sciences of the Czech Republic
Venue:
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Year:
2007

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Abstract

We propose a sequential forward feature selection method to find a subset of features that are most relevant to the classification task. Our approach uses novel estimation of the conditional mutual information between candidate feature and classes, given a subset of already selected features which is utilized as a classifier independent criterion for evaluation of feature subsets. The proposed mMIFS-U algorithm is applied to text classification problem and compared with MIFS method and MIFS-U method proposed by Battiti and Kwak and Choi, respectively. Our feature selection algorithm outperforms MIFS method and MIFS-U in experiments on high dimensional Reuters textual data.