Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
An introduction to variable and feature selection
The Journal of Machine Learning Research
Cancer gene search with data-mining and genetic algorithms
Computers in Biology and Medicine
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
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Microarray data is often characterized by high dimension and small sample size. Gene ranking is one of the most widely explored techniques to reduce the dimension because of its simplicity and computational efficiency. Many ranking methods have been suggested which depict their efficiency dependent upon the problem at hand. We have investigated the performance of six ranking methods on eleven cancer microarray datasets. The performance is evaluated in terms of classification accuracy and number of genes. Experimental results on all dataset show that there is significant variation in classification accuracy which depends on the choice of ranking method and classifier. Empirical results show that Brown Forsythe test statistics and Mutual Information method exhibit high accuracy with few genes whereas Gini Index and Pearson Coefficient perform poorly in most cases.