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Binary Whale Optimization Algorithm and Binary Moth Flame Optimization with Clustering Algorithms for Clinical Breast Cancer Diagnoses

Author

Listed:
  • Gehad Ismail Sayed

    (Cairo University, Egypt Scientific Research Group in Egypt (SRGE))

  • Ashraf Darwish

    (Helwan University Scientific Research Group in Egypt (SRGE))

  • Aboul Ella Hassanien

    (Cairo University, Egypt Scientific Research Group in Egypt (SRGE))

Abstract

Models based on machine learning algorithms have been developed to detect the breast cancer disease early. Feature selection is commonly applied to improve the performance of these models through selecting only relevant features. However, selecting relevant features in unsupervised learning is much difficult. This is due to the absence of class labels that guide the search for relevant information. This kind of the problem has rarely been studied in the literature. This paper presents a hybrid intelligence model that uses the cluster analysis algorithms with bio-inspired algorithms as feature selection for analyzing clinical breast cancer data. A binary version of both moth flame optimization and whale optimization algorithm is proposed. Two evaluation criteria are adopted to evaluate the proposed algorithms: clustering-based measurements and statistics-based measurements. The experimental results positively demonstrate that the capability of the proposed bio-inspired feature selection algorithms to produce both meaningful data partitions and significant feature subsets.

Suggested Citation

  • Gehad Ismail Sayed & Ashraf Darwish & Aboul Ella Hassanien, 2020. "Binary Whale Optimization Algorithm and Binary Moth Flame Optimization with Clustering Algorithms for Clinical Breast Cancer Diagnoses," Journal of Classification, Springer;The Classification Society, vol. 37(1), pages 66-96, April.
    • Handle: RePEc:spr:jclass:v:37:y:2020:i:1:d:10.1007_s00357-018-9297-3
    DOI: 10.1007/s00357-018-9297-3
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    References listed on IDEAS

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    1. Douglas Steinley & Michael J. Brusco, 2007. "Initializing K-means Batch Clustering: A Critical Evaluation of Several Techniques," Journal of Classification, Springer;The Classification Society, vol. 24(1), pages 99-121, June.
    2. Douglas Steinley & Michael Brusco, 2008. "Selection of Variables in Cluster Analysis: An Empirical Comparison of Eight Procedures," Psychometrika, Springer;The Psychometric Society, vol. 73(1), pages 125-144, March.
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    Cited by:

    1. Amitkumar Patil & Gunjan Soni & Anuj Prakash, 2022. "A BMFO-KNN based intelligent fault detection approach for reciprocating compressor," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(2), pages 797-809, June.
    2. Abrar Yaqoob & Rabia Musheer Aziz & Navneet Kumar Verma & Praveen Lalwani & Akshara Makrariya & Pavan Kumar, 2023. "A Review on Nature-Inspired Algorithms for Cancer Disease Prediction and Classification," Mathematics, MDPI, vol. 11(5), pages 1-32, February.

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