ENHANCE ACCURACY OF TUMOR CLASSIFICATION FROM GENE EXPRESSION OF MICROARRAY

Abstract

Gene expression microarray data is one of the most popular for dianosis 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.