Application of Granular Computing in Microarray Gene Expression Data

Feature selection is an important preprocessing technique for many pattern recognition problems.When the number of features is very large while the number of samples is relatively small as in the microarray data analysis,feature selection is even more important.A feature selection algorithm based on a granular computing and SVM-RFE hybrid algorithm can effectively eliminate most of the irrelevant genes,and can find a more informative gene subset in which the number of informative genes is almost least but its classification performance is almost highest.