Neuro-Fuzzy Ant Bee Colony Based Feature Selection for Cancer Classification

  • S. Gilbert Nancy
  • K. Saranya
  • S. Rajasekar
Conference paper
Part of the EAI/Springer Innovations in Communication and Computing book series (EAISICC)


A neuro-fuzzy expert system is multi-objective, which hybrids Ant Bee Colony (ABC) with Adaptive Neuro-Fuzzy Inference System (ANFIS) called NF-ABC, which improves the classification accuracy and reduces the complexity of dimensionality, redundancy, and irrelevant data. In this proposed work, SVM and kNN algorithms are used for classification to classify the given micro array data. The results revealed that the proposed model is more successful than the previous model.


Ant Bee Colony ANFIS Neuro-fuzzy SVM k-Nearest neighbor 


  1. 1.
    J. Li, P. Duan, H. Sang, S. Wang, Z. Liu, P. Duan, An efficient optimization algorithm for resource-constrained steelmaking scheduling problems. IEEE Access 6, 33883–33894 (2018)CrossRefGoogle Scholar
  2. 2.
    Chabaa S et al., Application of adaptive neuro-fuzzy inference systems for analyzing non-gaussian signal, in 2009 International Conference on Multimedia Computing and Systems (IEEE Explore)Google Scholar
  3. 3.
    S.M. Odeh, Using an adaptive neuro-fuzzy inference system (AnFis) algorithm for automatic diagnosis of skin cancer. J. Commun. Computer 8, 751–755 (2011)Google Scholar
  4. 4.
    Y. Marinakis, A hybrid ACO-GRASP algorithm for clustering analysis. Ann. Oper. Res 188(1), 343–358 (2011)MathSciNetCrossRefGoogle Scholar
  5. 5.
    P. Ganesh Kumar, C. Rani, D. Devaraj, A. Albert Victorie, Hybrid Ant Bee algorithm for fuzzy expert system based sample classification. IEEE/ACM Trans. Comput. Biol. Bioinform. 11(2), 347–360 (2014)CrossRefGoogle Scholar
  6. 6.
    H. Shah, R. Ghazali, N. Mohd Nawi, Hybrid Ant Bee Colony Algorithm for Volcano Temperature Prediction (Springer, Berlin, 2012), pp. 453–465Google Scholar
  7. 7.
    J.S.R. Jang, C.T. Sun, E. Mizutani, Neuro-Fuzzy and Soft Computing (Prentice-Hall, Upper Saddle River, NJ, 1997)Google Scholar
  8. 8.
    X. Zong, Z. Yong, J. Li-Min, H. Wei-Li, Construct interpretable fuzzy classification system based on fuzzy clustering initialization. Int. J. Inform. Technol. 11(6), 91–107 (2005)Google Scholar
  9. 9.
    P. Woolf, Y. Wang, A fuzzy logic approach to analyzing gene expression data. Physiol. Genomics 3, 9–15 (2000)CrossRefGoogle Scholar
  10. 10.
    S. Vinterbo, Small, fuzzy and interpretable gene expression based classifiers. Bioinformatics 21(9), 1964–1970 (2005)CrossRefGoogle Scholar
  11. 11.
    A.C. Tan, D. Gilbert, Ensemble machine learning on gene expression data for cancer classification. Appl. Bioinform. 2, 75–83 (2003)Google Scholar
  12. 12.
    S. Haddou Bouazza, N. Hamdi, A. Zeroual, Gene-expression-based cancer classification through feature selection with KNN and SVM classifiers, in 2015 Intelligent Systems and Computer Vision (ISCV), vol. 1 (IEEE), pp. 1–6.
  13. 13.
    T.S. Furey, N. Cristianini, N. Duffy, D.W. Bednarski, M. Schummer, D. Haussler, Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16, 906–914 (2000)CrossRefGoogle Scholar
  14. 14.
    F. Chu, L. Wang, Applications of support vector machines to cancer classification with microarray data. Int. J. Neural Syst. 15(06), 475–484 (2005)CrossRefGoogle Scholar
  15. 15.
    G. Schaefer, Thermography based breast cancer analysis using statistical features and fuzzy classification. Pattern Recogn. 42(6), 1133–1137 (2009)CrossRefGoogle Scholar
  16. 16.
    UCI machine learning repository,

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • S. Gilbert Nancy
    • 1
  • K. Saranya
    • 1
  • S. Rajasekar
    • 1
  1. 1.Department of Computer Science and EngineeringBannari Amman Institute of TechnologySathyamangalamIndia

Personalised recommendations