Microarray cancer classification using feature extraction-based ensemble learning method

Microarray cancer datasets generally contain many features with a small number of samples, so initially we need to reduce redundant features to allow faster convergence. To address this issue, we proposed a novel feature extraction-based ensemble classification technique using support vector machine (SVM) which classifying microarray cancer data and helps to build intelligent systems for early cancer detection. Novelty of the proposed approach is described by classifying cancer data as follows: a) we extracted information by reducing the size of larger dataset using various feature selection techniques, such as, principal component analysis (PCA), chi-square, genetic algorithm (GA) and F-score; b) classifying extracted information in two samples as normal and malignant classes using majority voting ensemble SVM. In SVM ensemble-based approach we use different SVM kernels, like, linear, polynomial, radial basis function (RBF), and sigmoid. The calculated results of particular kernels are combined using majority voting approach. The effectiveness of the algorithm is validated on six benchmark cancer datasets viz. colon, ovarian, leukaemia, breast, lung and prostate using ensemble SVM classification.