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General Information
    • ISSN: 2010-3700
    • Frequency: Bimonthly
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Engineering & Technology Digital Library, Google Scholar, Crossref, ProQuest, Electronic Journals Library, DOAJ and EI (INSPEC, IET).
    • E-mail: ijmlc@ejournal.net
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2016 Vol.6(3): 184-190 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2016.6.3.596

ABC-SVM: Artificial Bee Colony and SVM Method for Microarray Gene Selection and Multi Class Cancer Classification

Hala M. Alshamlan, Ghada H. Badr, and Yousef A. Alohali
Abstract—In this paper, we propose apply ABC algorithm in analyzing microarray dataset. In addition, we propose an innovative hybrid classification model, Support Vector Machine (SVM) with ABC algorithm, to measure the classification accuracy for selected genes. We evaluate the performance of the proposed ABC-SVM algorithm by conducting extensive experiments on six binary and multi-class microarrays dataset. Furthermore, we compare our proposed ABC-SVM algorithm with previously known techniques. The experimental results prove that ABC-SVM algorithm is promising approach for solving gene selection and cancer classification problems, and achieves the highest classification accuracy together with the lowest average of selected genes compared to previously suggested methods.

Index Terms—ABC, gene selection, microarray, and SVM.

The authors are with Computer Science Department, King Saud University, Saudi Arabia (e-mail: halshamlan@ksu.edu.sa, badrghada@hotmail.com, yousef@ksu.edu.sa).

[PDF]

Cite: Hala M. Alshamlan, Ghada H. Badr, and Yousef A. Alohali, "ABC-SVM: Artificial Bee Colony and SVM Method for Microarray Gene Selection and Multi Class Cancer Classification," International Journal of Machine Learning and Computing vol. 6, no. 3, pp. 184-190, 2016.

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