Semi-supervised fuzzy K-NN for cancer classification from microarray gene expression data

Cancer classification from microarray gene expression data is a challenging task in computational biology and bioinformatics as the sufficient number of labeled samples (required to train the traditional classifiers) are very expensive and difficult to collect. Therefore, the predication accuracies of the classifiers trained with limited training samples are often very low. Although, the unlabeled samples are relatively inexpensive and readily available, traditional classifiers not generally utilize the distribution of those unlabeled samples. In this context, this article presents a novel `self-training´ based semi-supervised classification method using fuzzy K-Nearest Neighbour algorithm which utilizes the unlabeled samples along with the labeled samples to improve the prediction accuracy of the cancer classification. The proposed method is evaluated with a number of microarray gene expression cancer data sets. Experimental results justify the potentiality of the proposed semi-supervised method for cancer classification using microarray gene expression data in comparison to its other supervised counterparts.