Classification of Microarray Data Using Kernel Based Classifiers

Received: 8 April 2019 Accepted: 4 June 2019 Microarray dataset enables scientists to genotype thousands of loci at a time, making it easier to determine the association between chromosomal regions and particular diseases. This paper mainly compares the performance of different classifers on microarray data. Firstly, the expressed genes related to ovarian cancer were identified through a statistical test. Next, various classifiers, namely, Extreme Learning Machine (ELM) and Relevance Vector Machine (RVM), were applied to categorize the datasets and samples into malignant or benign classes. Then, the performance of each classifier was measured by precision, recall, specificity, etc. The results show that the ELM and the RVM are better classifiers in comparison to the support vector machine (SVM). The research results lay the basis for the application of kernel-based classifiers in cancer identification.

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