Using dimension reduction with feature selection to enhance accuracy of tumor classification

Gene expression microarray data is one of the most popular for diagnosis of cancer. However, the microarray data have thousands of genes and very few samples, it is crucial to develop techniques to effectively gene selection for analysis. So, dimension reduction is an important issue for analysis, 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 enhance classifier accuracy rates on the gene expression microarray data. As a case study, we have considered PCA for dimension reduction, decesion tree algorithms (DT) for feature selection, and then Multi Layer Perceptron network (MLP) for classification. Experimental results illustrate that our proposed framework is effective to enhance classification accuracy rates.