Table of Contents
International Scholarly Research Notices
Volume 2014, Article ID 769159, 18 pages
http://dx.doi.org/10.1155/2014/769159
Research Article

Classification of Microarray Data Using Kernel Fuzzy Inference System

Department of Computer Science and Engineering, NIT Rourkela, Rourkela, Odisha 769008, India

Received 28 March 2014; Revised 28 May 2014; Accepted 12 June 2014; Published 21 August 2014

Academic Editor: Wen-Sheng Chen

Copyright © 2014 Mukesh Kumar and Santanu Kumar Rath. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The DNA microarray classification technique has gained more popularity in both research and practice. In real data analysis, such as microarray data, the dataset contains a huge number of insignificant and irrelevant features that tend to lose useful information. Classes with high relevance and feature sets with high significance are generally referred for the selected features, which determine the samples classification into their respective classes. In this paper, kernel fuzzy inference system (K-FIS) algorithm is applied to classify the microarray data (leukemia) using t-test as a feature selection method. Kernel functions are used to map original data points into a higher-dimensional (possibly infinite-dimensional) feature space defined by a (usually nonlinear) function through a mathematical process called the kernel trick. This paper also presents a comparative study for classification using K-FIS along with support vector machine (SVM) for different set of features (genes). Performance parameters available in the literature such as precision, recall, specificity, F-measure, ROC curve, and accuracy are considered to analyze the efficiency of the classification model. From the proposed approach, it is apparent that K-FIS model obtains similar results when compared with SVM model. This is an indication that the proposed approach relies on kernel function.