Combining statistical and Fuzzy-rough classifiers for cancer Subtype prediction
Paper Details
Combining statistical and Fuzzy-rough classifiers for cancer Subtype prediction
Abstract
Cancer prediction from gene expression data is one of the challenging areas of research in the field of bioinformatics and machine learning. In gene expression data, labeled samples are very limited compared to unlabeled samples; and labeling of unlabeled data is expensive. Therefore, single classifier trained with limited training samples often fails to produce desired result. In this situation, combination of classifiers can be effective as its ensembles the results of individual classifiers which can improve the cancer prediction accuracy. In this article a novel method, combining statistical and fuzzy-rough classifiers (CSFRC) for cancer prediction is proposed which uses support vector machine, naive bayes as statistical classifiers and fuzzy-rough nearest neighbor classifier. The proposed method is able to deal the uncertainty, overlapping and indiscernibility usually present in cancer subtype classes of the gene expression data. The proposed method is validated on eight publicly available gene expression datasets. Experimental results suggest that the performance of the proposed method provides better results in comparison to other compared classifiers for cancer subtype prediction from gene expression data. The proposed method turns out to be very effective in cancer prediction from gene expression data particularly when the individual classifier result is not up to the mark with limited training samples.
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