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IJMLC 2020 Vol.10(3): 458-464 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.3.957

Opposition-Based Enhanced Grey Wolf Optimization Algorithm for Feature Selection in Breast Density Classification

Rahul Hans and Harjot Kaur

Abstract—Breast Cancer is the most widespread cancer amongst women in the world. An increase in the density of breast may lead to an increase in the risk of breast cancer. For the accurate diagnosis of breast cancer, these days Machine learning-based computer-aided diagnosis systems are widely used to assist the radiologists. However, there is still a scope of improvement in the computer-aided diagnosis system for the feature selection problem, which is considered as an optimization problem. Optimization techniques are the methods where the finest solution to a problem is found by using random search mechanism. Grey wolf optimization is one of the most recent optimization algorithms. This paper presents an enhanced version of Grey Wolf Optimization algorithm and tests the performance of the proposed version of the algorithm on thirteen different benchmark functions and the binary version of the proposed algorithm is used for selecting optimal number of features for breast density classification problem.

Index Terms—Breast density classification, feature selection grey wolf optimization, opposition based learning.

Rahul Hans is with the Department of Computer Science and Engineering, DAV University, Jalandhar and also with Guru Nanak Dev University, Amritsar, Punjab, India (e-mail: rahulhans@gmail.com).
Harjot Kaur is with the Department of Computer Science and Engineering, Guru Nanak Dev University, Regional Campus, Gurdaspur, Punjab India (e-mail: harjotkaursohal@rediffmail.com)

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Cite: Rahul Hans and Harjot Kaur, "Opposition-Based Enhanced Grey Wolf Optimization Algorithm for Feature Selection in Breast Density Classification," International Journal of Machine Learning and Computing vol. 10, no. 3, pp. 458-464, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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