Reduction combination determination for efficient microarray data classification with three stage dimensionality reduction approach

Abstract

Classification of microarray data with high dimension and small sample size is a complex task. This work explores the optimal search space appropriate for classification. Here the crush of dimensionality is handled with a three stages dimension reduction technique. At the first stage, statistical measures are used to remove genes that do not contribute for classification. In the second stage, more noisy genes are removed by considering signal to noise ratio (SNR). In the third stage, principal component analysis (PCA) method is used to further reduce the dimension. Further, how much to reduce at each stage is crucial to develop an efficient classifier. Combination of different proportion of reduction at each stage is considered in this study to find appropriate combination for each dataset which maximizes the classifier performance. Help of naïve Bayes classifier is taken here to find appropriate combination of reduction.