Reducing error of tumor classification by using dimension reduction with feature selection

Dimension reduction is an important issue for analysis of gene expression microarray data, of which principle component analysis (PCA) is one of the frequently used methods, and in the previous works, the top several principle components are selected for modeling according to the descending order of eigenvalues. While in this paper, we argue that not all the first features are useful, but features should be selected form all the components by feature selection methods. We demonstrate a framework for selecting good feature subsets from all the principle components, leading to reduced classifier error rates on the gene expression microarray data. As a case study, we have considered PCA for dimension reduction, genetic algorithms and the floating backward search method for feature selection, and support vector machines for classification. Experimental results illustrate that our proposed framework is effective to reduce classification error rates.