Efficient Microarray Data Classification with Three-Stage Dimensionality Reduction

High dimensionality and small sample size are the intrinsic nature of microarray data, which require effective computational methods to discover useful knowledge from it. Classification of microarray data is one of the important tasks in this field of work. Representation of the search space with thousands of genes makes this work much complex and difficult to classify efficiently. In this work, three different stages have been adopted to handle the crush of dimensionality and classify the microarray data. 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. Finally, these reduced datasets are presented to different classification techniques to evaluate their performance. Here, four different classification algorithms are used such as artificial neural network (ANN), naïve Bayesian classifier, multiple linear regression (MLR), and k-nearest neighbor (k-NN) to validate the benefits of three-stage dimensionality reduction. The experimental results show that the use of statistical methods, SNR, and PCA improves the overall performance of the classifiers.