A correlation based SVM-recursive multiple feature elimination classifier for breast cancer disease using microarray

Support Vector Machine (SVM) [2], is most widely popular learning algorithm used for classification of large dataset. Our project aims to generate a classifier for breast cancer genes microarray by using modified-SVM-RFE algorithm. This breast cancer microarray contains a large number of genes and its expression, so it necessary to reduce the number of genes before applying for classification. So the most efficient algorithm that can be applied for classification of microarray is SVM-RFE [3][8], which is an embedded method, which performs backward single gene elimination as well as classification of the dataset. A new modified algorithm is proposed with less computation over SVM-RFE. SVM-RFE generates the rank of the features and eliminates one lowest rank irrelevant feature, in each iteration. Since our microarray contains 47,294 genes its very computational overhead to reduce the dimension. So the modified algorithm which removes more than one irrelevant genes in single iteration of SVM-RFE algorithm. And also this algorithm only removes irrelevant gene, it does not remove the correlated genes. So before applying SVM-RFE, our research focuses on finding out the correlated genes and extracting a new gene from the two, and then apply SVM-RFE on the new set of genes. So our proposed method is Correlation based Support Vector Machine Recursive Multiple Feature Elimination (CSVM-RMFE) algorithm which first extracts a new genes from two correlated genes called virtual gene and then apply SVM-RMFE to generate a classifier. This SVM-RMFE algorithm eliminate multiple feature so that the classification time can be reduced and its accuracy can be increased.