Abstract
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, Naive bayes and support vector machine (SVM). The performance of the proposed method is compared with the conventional classifiers like SVM, nearest neighbor and Naive bayes. Discrete wavelet transform and moving window technique is used for feature selection. 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.
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