A graph-based approach to feature selection
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Feature Selection for Gender Classification
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Mutual information criteria for feature selection
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
Computer Methods and Programs in Biomedicine
Hypergraph based information-theoretic feature selection
Pattern Recognition Letters
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In this paper, a new greedy feature selection algorithm is proposed to detect more precisely informative features. It overcomes the limitation of many existing MI-based gready feature selection algorithms. It is capable of detecting the relation of relevant feature combinations in some degree.In addition, the requirements of the memory storage and computation cost are low. Experimental results for the UCI benchmark dataset demonstrate the good performance of the proposed algorithm on the experimented data sets.