American Journal of Applied Sciences

Efficient Classification of Cancer using Support Vector Machines and Modified Extreme Learning Machine based on Analysis of Variance Features

A. Bharathi and A.M. Natarajan

DOI : 10.3844/ajassp.2011.1295.1301

American Journal of Applied Sciences

Volume 8, Issue 12

Pages 1295-1301

Abstract

Problem statement: The primary objective is to propose efficient cancer classification techniques which provide reliable and significant classification accuracy. To achieve this primary research goal is to find the smallest set of genes that can ensure high accuracy in classification using supervised machine learning algorithms. The significance of finding the minimum subset is three fold: (a) The computational burden and noise arising from irrelevant genes are much reduced; (b) the cost for cancer testing is reduced significantly as it simplifies the gene expression tests to include only a very small number of genes rather than thousands of genes; (c) it calls for more investigation into the probable biological relationship between these small numbers of genes and cancer development and treatment. Approach: The proposed method involves two steps. In the first step, some important genes are chosen with the help of Analysis of Variance (ANOVA) ranking scheme. In the second step, the classification capability is tested for all simple combinations of those important genes using a better classifier. Results: The proposed method initially uses Support Vector Machine (SVM) classifier. Then Modified Extreme Learning Machine classifier is used for increasing the classification accuracy over SVM. Conclusion: The two datasets are used (Lymphoma and Liver cancer) in the experimental result shows that the proposed method performs the cancer classification with better accuracy when compared to the SVM methods.

Copyright

© 2011 A. Bharathi and A.M. Natarajan. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.