Performance analysis of microarray data classification using machine learning techniques

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Subhendu Kumar Pani

Biju Patnaik University of Technology, India

: J Comput Eng Inf Technol

Abstract

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Microarray technology of DNA permits simultaneous monitoring and determining of thousands of gene expression activation levels in a single experiment. Data mining techniques such as classification is extensively used on microarray data for medical diagnosis and gene analysis. However, high dimensionality of the data affects the performance of classification and prediction. Consequently, a key issue in microarray data is feature selection and dimensionality reduction in order to achieve better classification and predictive accuracy. There are several machine learning approaches available for feature selection. In this study, the authors use particle swarm optimization (PSO) and genetic algorithm (GA) to find the performance of several popular classifiers on a set of microarray datasets. Experimental results conclude that feature selection affects the performance.

Biography

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