Microarray data classification using Fuzzy K-Nearest Neighbor

Microarray dataset may contain a huge number of insignificant and irrelevant features that might lead to loss of useful information. The classes with both high relevance and having high significance feature sets are generally preferred for selecting the features, which determines the sample classification into their respective classes. This property has gained a lot of significance among the researchers and practitioners in DNA microarray classification. In this paper, K-Nearest Neighbor (K-NN) and Fuzzy K-Nearest Neighbor (Fuzzy K-NN) algorithms are used to classify microarray data sets using t-test as a feature selection method. Further, this paper presents a comparative analysis on the obtained classification accuracy by coupling Fuzzy K-NN along with K-NN and other existing models available in the literature. Performance parameters available in literature such as: precision, recall, specificity, F-Measure, ROC curve and accuracy are used in this comparative analysis to analyze the behavior of the classifiers. From the proposed approach, it is apparent that Fuzzy K-NN model is the most suitable classification model among K-NN and other classifiers.