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The Scientific World Journal
Volume 2014, Article ID 195470, 9 pages
Research Article

A Discrete Wavelet Based Feature Extraction and Hybrid Classification Technique for Microarray Data Analysis

1Department of Computer Science and Engineering, RMK Engineering College, Anna University, Chennai, India
2Department of Information Science and Technology, Anna University, Chennai, India

Received 22 January 2014; Revised 20 June 2014; Accepted 2 July 2014; Published 6 August 2014

Academic Editor: Liyuan Li

Copyright © 2014 Jaison Bennet et al. 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.


Cancer classification by doctors and radiologists was based on morphological and clinical features and had limited diagnostic ability in olden days. The recent arrival of DNA microarray technology has led to the concurrent monitoring of thousands of gene expressions in a single chip which stimulates the progress in cancer classification. In this paper, we have proposed a hybrid approach for microarray data classification based on nearest neighbor (KNN), naive Bayes, and support vector machine (SVM). Feature selection prior to classification plays a vital role and a feature selection technique which combines discrete wavelet transform (DWT) and moving window technique (MWT) is used. The performance of the proposed method is compared with the conventional classifiers like support vector machine, nearest neighbor, and naive Bayes. Experiments have been conducted on both real and benchmark datasets and the results indicate that the ensemble approach produces higher classification accuracy than conventional classifiers. This paper serves as an automated system for the classification of cancer and can be applied by doctors in real cases which serve as a boon to the medical community. This work further reduces the misclassification of cancers which is highly not allowed in cancer detection.