IDEAS home Printed from https://ideas.repec.org/a/spr/jclass/v37y2020i1d10.1007_s00357-018-9297-3.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00357-018-9297-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00357-018-9297-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jerzy Korzeniewski, 2016. "New Method Of Variable Selection For Binary Data Cluster Analysis," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 17(2), pages 295-304, June.
    2. Jerzy Korzeniewski, 2016. "New Method Of Variable Selection For Binary Data Cluster Analysis," Statistics in Transition New Series, Polish Statistical Association, vol. 17(2), pages 295-304, June.
    3. Simon Wiersma & Dr. Michael Heinrich & Prof. Dr. Tobias Just, 2018. "La Aplicación del Análisis Clúster en los Mercados Inmobiliarios," LARES lares_2018_paper_23-heinr, Latin American Real Estate Society (LARES).
    4. Faicel Chamroukhi, 2016. "Piecewise Regression Mixture for Simultaneous Functional Data Clustering and Optimal Segmentation," Journal of Classification, Springer;The Classification Society, vol. 33(3), pages 374-411, October.
    5. Jiang, Yawen & Jia, Caiyan & Yu, Jian, 2013. "An efficient community detection method based on rank centrality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 2182-2194.
    6. J. Fernando Vera & Rodrigo Macías, 2021. "On the Behaviour of K-Means Clustering of a Dissimilarity Matrix by Means of Full Multidimensional Scaling," Psychometrika, Springer;The Psychometric Society, vol. 86(2), pages 489-513, June.
    7. Nathan Cunningham & Jim E. Griffin & David L. Wild, 2020. "ParticleMDI: particle Monte Carlo methods for the cluster analysis of multiple datasets with applications to cancer subtype identification," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(2), pages 463-484, June.
    8. Pacheco, Joaquín & Casado, Silvia & Porras, Santiago, 2013. "Exact methods for variable selection in principal component analysis: Guide functions and pre-selection," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 95-111.
    9. Gianluigi Migliavacca & Marco Rossi & Dario Siface & Matteo Marzoli & Hakan Ergun & Raúl Rodríguez-Sánchez & Maxime Hanot & Guillaume Leclerq & Nuno Amaro & Aleksandr Egorov & Jawana Gabrielski & Björ, 2021. "The Innovative FlexPlan Grid-Planning Methodology: How Storage and Flexible Resources Could Help in De-Bottlenecking the European System," Energies, MDPI, vol. 14(4), pages 1-28, February.
    10. Michael Brusco & Douglas Steinley, 2015. "Affinity Propagation and Uncapacitated Facility Location Problems," Journal of Classification, Springer;The Classification Society, vol. 32(3), pages 443-480, October.
    11. Michael Brusco & Douglas Steinley, 2007. "A Comparison of Heuristic Procedures for Minimum Within-Cluster Sums of Squares Partitioning," Psychometrika, Springer;The Psychometric Society, vol. 72(4), pages 583-600, December.
    12. Tom Wilderjans & Dirk Depril & Iven Van Mechelen, 2013. "Additive Biclustering: A Comparison of One New and Two Existing ALS Algorithms," Journal of Classification, Springer;The Classification Society, vol. 30(1), pages 56-74, April.
    13. Junqi Wang & Rundong Liu & Linfeng Zhang & Hussain Syed ASAD & Erlin Meng, 2019. "Triggering Optimal Control of Air Conditioning Systems by Event-Driven Mechanism: Comparing Direct and Indirect Approaches," Energies, MDPI, vol. 12(20), pages 1-20, October.
    14. Gehad Ismail Sayed & Ashraf Darwish & Aboul Ella Hassanien, 2018. "A New Chaotic Whale Optimization Algorithm for Features Selection," Journal of Classification, Springer;The Classification Society, vol. 35(2), pages 300-344, July.
    15. Gao, Jinxin & Hitchcock, David B., 2010. "James-Stein shrinkage to improve k-means cluster analysis," Computational Statistics & Data Analysis, Elsevier, vol. 54(9), pages 2113-2127, September.
    16. Meldrum, James R. & Champ, Patricia A. & Bond, Craig A., 2013. "Heterogeneous nonmarket benefits of managing white pine bluster rust in high-elevation pine forests," Journal of Forest Economics, Elsevier, vol. 19(1), pages 61-77.
    17. Antonello Maruotti & Antonio Punzo, 2021. "Initialization of Hidden Markov and Semi‐Markov Models: A Critical Evaluation of Several Strategies," International Statistical Review, International Statistical Institute, vol. 89(3), pages 447-480, December.
    18. Joeri Hofmans & Eva Ceulemans & Douglas Steinley & Iven Mechelen, 2015. "On the Added Value of Bootstrap Analysis for K-Means Clustering," Journal of Classification, Springer;The Classification Society, vol. 32(2), pages 268-284, July.
    19. Juan José Fernández-Durán & María Mercedes Gregorio-Domínguez, 2021. "Consumer Segmentation Based on Use Patterns," Journal of Classification, Springer;The Classification Society, vol. 38(1), pages 72-88, April.
    20. Magali A. Delmas & Sanja Pekovic, 2018. "Corporate Sustainable Innovation and Employee Behavior," Journal of Business Ethics, Springer, vol. 150(4), pages 1071-1088, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jclass:v:37:y:2020:i:1:d:10.1007_s00357-018-9297-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.