Feature Selection and Classification of Microarray Data Using Machine Learning Techniques
Microarray based gene expression profiling has emerged as an efficient technique for classification, diagnosis, prognosis, and treatment of cancer. The major drawback in microarray data is the "curse of dimensionality problem," which hinders the useful information of a data set and leads to computational instability. Therefore, selecting relevant genes is a challenging task in microarray data analysis. Most of the existing schemes employ a two stage process: feature selection (FS) followed by classification. Various methods such as t test, F test, Wilcoxon test, signal to noise ratio, kh 2 test, information gain (IG) Gini index, and Fisher score are used for selecting the significant features. Moreover, different classifiers like logistic regression, naive Bayes, K nearest neighbor, artificial neural network, radial basis function network, probabilistic neural network, and support vector machine have already been executed for classifying the data set. In this chapter, a permutation of several FS methods and classifiers has been implemented independently, and the finest pair is chosen based on experimental analysis using certain performance parameters for the designed classifiers. The obtained results are compared with existing schemes. The main objective of this study is to survey the already applied intelligent techniques and point out their limitations and advantages.