Title: Linear regression-based feature selection for microarray data classification

Authors: Md. Abid Hasan; Md. Kamrul Hasan; M. Abdul Mottalib

Addresses: Department of Computer Science and Engineering (CSE), Islamic University of Technology (IUT), Gazipur 1704, Bangladesh ' Department of Computer Science and Engineering (CSE), Islamic University of Technology (IUT), Gazipur 1704, Bangladesh ' Department of Computer Science and Engineering (CSE), Islamic University of Technology (IUT), Gazipur 1704, Bangladesh

Abstract: Predicting the class of gene expression profiles helps improve the diagnosis and treatment of diseases. Analysing huge gene expression data otherwise known as microarray data is complicated due to its high dimensionality. Hence the traditional classifiers do not perform well where the number of features far exceeds the number of samples. A good set of features help classifiers to classify the dataset efficiently. Moreover, a manageable set of features is also desirable for the biologist for further analysis. In this paper, we have proposed a linear regression-based feature selection method for selecting discriminative features. Our main focus is to classify the dataset more accurately using less number of features than other traditional feature selection methods. Our method has been compared with several other methods and in almost every case the classification accuracy is higher using less number of features than the other popular feature selection methods.

Keywords: linear regression; feature selection; microarray data classification; high dimensionality; data mining; bioinformatics; gene expression profiles.

DOI: 10.1504/IJDMB.2015.066776

International Journal of Data Mining and Bioinformatics, 2015 Vol.11 No.2, pp.167 - 179

Received: 25 Mar 2013
Accepted: 30 Jul 2013
Published online: 05 Jan 2015 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article