Discriminative Gene Selection Employing Linear Regression Model

Abid Hasan; Shaikh Jeeshan Kabeer; Kamrul Hasan

Discriminative Gene Selection Employing Linear Regression Model

Abid Hasan * §

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Shaikh Jeeshan Kabeer

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Kamrul Hasan

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mottalib

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§ Islamic University of Technology (IUT), Dhaka, Bangl...

Discriminative Gene Selection Employing Linear Regression Model

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Abstract

Microarray datasets enables the analysis of expression of thousands of genes across hundreds of samples. Usually classifiers do not perform well for large number of features (genes) as is the case of microarray datasets. That is why a small number of informative and discriminative features are always desirable for efficient classification. Many existing feature selection approaches have been proposed which attempts sample classification based on the analysis of gene expression values. In this paper a linear regression based feature selection algorithm for two class microarray datasets has been developed which divides the training dataset into two subtypes based on the class information. Using one of the classes as the base condition, a linear regression based model is developed. Using this regression model the divergence of each gene across the two classes are calculated and thus genes with higher divergence values are selected as important features from the second subtype of the training data. The classification performance of the proposed approach is evaluated with SVM, Random Forest and AdaBoost classifiers. Results show that the proposed approach provides better accuracy values compared to other existing approaches i.e. Relief F, CFS, decision tree based attribute selector and attribute selection using correlation analysis.

References

17 Cites in Article

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Abid Hasan, Shaikh Jeeshan Kabeer, Kamrul Hasan, mottalib. 2013. "Discriminative Gene Selection Employing Linear Regression Model". Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 13 (GJCST Volume 13 Issue C4).

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GJCST Volume 13 Issue C4

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

Classification feature selection linear regression microarray dataset

Classification

GJCST-C Classification D.2.2

Submission ReceivedDecember 16, 2012
Peer Review Double Blind
Handling Editor
Accepted January 3, 2013
Published January 15, 2013

Version of record

v1.2

Issue date

May 2, 2013

Language

English

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Article Matrices

Total Score: 144

Country: Bangladesh

Subject: Global Journal of Computer Science and Technology

Authors: Abid Hasan, Shaikh Jeeshan Kabeer, Kamrul Hasan, Md. Abdul Mottalib (PhD/Dr. count: 0)

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