An approach to overcome imbalance datasets of eukaryotic genomes during the analysis by machine learning technique (SVM)

Mohd  Faheem Khan; Gaurav Chauhan   and A  K  Jaitly

doi:10.17485/ijst/2011/v4i5.2

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An approach to overcome imbalance datasets of eukaryotic genomes during the analysis by machine learning technique (SVM)

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Indian Journal of Science and Technology

DOI: 10.17485/ijst/2011/v4i5.2

Year: 2011, Volume: 4, Issue: 5, Pages: 520-524

Original Article

An approach to overcome imbalance datasets of eukaryotic genomes during the analysis by machine learning technique (SVM)

Mohd. Faheem Khan^{1, 2} , Gaurav Chauhan² and A. K. Jaitly^1*

¹Department of Plant Sciences, MJP Rohilkhand University Bareilly (U.P) India.
²Bioinformatics Centre, IVRI, Izatnagar, Bareilly-243122 (U.P) India.
[email protected]

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

In biology, Support Vector Machines (SVM) is most frequently used tool for the analysis of gene expression, microarray experiments and other biological applications. In human genome dataset, only a small proportion of the DNA sequences represent genes, and the rest do not. In our work, we highlighted the reasons why, particular SVM, fails and what can be done to overcome this.
Keywords: Imbalanced dataset, BioSVM