Elements of information theory
Elements of information theory
The nature of statistical learning theory
The nature of statistical learning theory
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Using Rough Sets with Heuristics for Feature Selection
Journal of Intelligent Information Systems
Machine learning in DNA microarray analysis for cancer classification
APBC '03 Proceedings of the First Asia-Pacific bioinformatics conference on Bioinformatics 2003 - Volume 19
Robust feature selection by mutual information distributions
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Estimating optimal feature subsets using efficient estimation of high-dimensional mutual information
IEEE Transactions on Neural Networks
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Cancer classification is one major application of microarray data analysis. Due to the ultra high dimension of gene expression data, efficient feature selection methods are in great needs for selecting a small number of informative genes. In this paper, we propose a novel feature selection method MIRS based on mutual information and rough set. First, we select some top-ranked features which have higher mutual information with the target class to predict. Then rough set theory is applied to remove the redundancy among these selected genes. Binary particle swarm optimization (BPSO) is first proposed for attribute reduction in rough set. Finally, the effectiveness of the proposed method is evaluated by the classification accuracy of SVM classifier. Experiment results show that MIRS is superior to some other classical feature selection methods and can get higher prediction accuracy with small number of features. Generally, the results are highly promising.