Journal of Data Mining and Knowledge Engineering ISSN: 3139-5341 (Online)

Abstract

Microarray technology is one of the most challenging areas where researchers study genetics and organisms. Microarray data are a subset of biomedical data that have huge dimensions. The real challenge that such microarray data pose is its enormous features with a very low sample space. This paper presents various pre-processing techniques on microarray data for Feature Selection and Classification. Several microarray datasets were taken for experimental analysis, where both binary and multi-class classification was performed. Extensively large numbers of features were successfully reduced while achieving good classification accuracy.

Keywords: - Microarray data, data pre-processing, Principal Component Analysis (PCA), Analysis of Variance (ANOVA), Recursive feature elimination (RFE), Feature Selection, Dimension Reduction, Machine Learning Algorithms.