An efficient SVM based tumor classification with symmetry Non-negative Matrix Factorization using gene expression data

Abstract

A reliable and accurate identification of the type of tumors is crucial to the proper treatment of cancers. The classification of tumors was and is both a practical and theoretic necessity and requirement. DNA microarrays provide a new technique of measuring gene expression, which has attracted a lot of research interest in recent years. It was suggested that gene expression data from microarrays (biochips) can be employed in many biomedical areas, e.g., in cancer classification. Although several, new and existing, methods of classification were tested, a selection of proper (optimal) set of genes, the expressions of which can serve during classification, is still an open problem. This paper presents a new method for tumor classification using gene expression data. In the proposed method, we first select genes using Nonnegative Matrix Factorization (NMF). In order to improve the performance of classification, Symmetry NMF (SymNMF) is used in this approach. Then, features are extracted from the selected genes by virtue SymNMF. As a last step, an efficient machine learning approach is used to classify the tumor samples using the extracted features. In order for a better classification, Support Vector Machine with Weighted Kernel Width (WSVM) is used in this classification approach. The performance of the proposed approach is tested using colon cancer data set and the acute leukemia data set. It is observed from the experimental results that the proposed approach provides better performance when compared with the traditional approaches.