An efficient approach for feature extraction and classification of microarray cancer data. (1st January 2014)

• Record Type: Journal Article
• Authors: Bai, Anita; Pradhan, Anima
• Is Part Of: International journal of computational intelligence studies. Volume 3:Number 4(2014)
• Page Start: 339
• Page End: 355
• Publication Date: 2014-01-01
• Subjects: cancer classification -- feature extraction -- principal components -- principal component analysis -- PCA -- neural networks -- NN -- support vector machine -- SVM; Computational intelligence -- Periodicals; 006.305
• DOI: 10.1504/IJCISTUDIES.2014.067034
• Languages: English
• ISSNs: 1755-4985

Abstract

DNA microarray consists of huge amount of features with small number of samples. In this paper, we address the dimension reduction of DNA features in which relevant features are extracted among thousands of irrelevant ones through dimensionality reduction. This enhances the speed and accuracy of the classifiers. Principal component analysis (PCA) is a very powerful statistical technique, is used to satisfy the aim, is to project the original I-dimensional space into an I 0 dimensional linear subspace, where I > I 0 such that the variance in the data is maximally explained within the smaller I 0 dimensional space to solve the curse of dimensionality problem. Neural networks (NN) and support vector machine (SVM) are implemented and their performances are measured and compared in terms of predictive accuracy, specificity and sensitivity. In our first contribution, we implemented PCA for significant feature extraction and then implement FFNN trained using back propagation (BP) and SVM on the reduced feature set. In the second part, we attempt to validate our results on three public data sets viz., leukaemia, ovarian and colon cancer data.