Hybrid ACO Chaos-Assisted Support Vector Machines for Classification of Medical Datasets

  • Gunjan Mishra
  • Vivek Ananth
  • Kalpesh Shelke
  • Deepak Sehgal
  • Jayaraman Valadi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 336)


There is a need for developing accurate learning algorithms for analyzing large-scale medical diagnostic, prognostic, and treatment datasets. Success of classifiers like support vector machines lies in employment of best informative features out of a huge noisy feature space. In this work, we employ a hybrid filter–wrapper approach to build high-performance classification models. We tested our algorithms using popular datasets containing clinic-bio-pathological parameters of leukemia, hepatitis, breast cancer, and colon cancer taken from publically available datasets. Our results indicate that the hybrid algorithm can discover informative subsets possessing very high classification accuracy.


Feature selection Ant colony optimization Filter–wrapper hybrid algorithm Medical dataset 


  1. 1.
    Baek, S., Tsai, C.-A., Chen, J.J.: Development of biomarker classifiers from high-dimensional data. Brief. Bioinform. 10, 537–546 (2009)CrossRefGoogle Scholar
  2. 2.
    Poncelet, P., Masseglia, F., Teisseire, M.: Successes and New Directions in Data Mining. IGI Global, Hershey (2008)Google Scholar
  3. 3.
    Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)zbMATHGoogle Scholar
  4. 4.
    John, G.H., Kohavi, R., Pfleger, K.: Irrelevant features and the subset selection problem. In; Proceedings of the Eleventh International Conference on Machine Learning, pp. 121–129 (1994)Google Scholar
  5. 5.
    Saeys, Y., Inza, I., Larrañaga, P.: A review of feature selection techniques in bioinformatics. Bioinformatics 23, 2507–2517 (2007)CrossRefGoogle Scholar
  6. 6.
    Chrysostomou, K.: Encyclopedia of Data Warehousing and Mining, 2nd edn. IGI Global, Hershey (2008)Google Scholar
  7. 7.
    Liu, H., Motoda, H. (eds.): Feature Extraction, Construction and Selection. Springer, Boston (1998)zbMATHGoogle Scholar
  8. 8.
    Ahmad, F., Isa, N.A.M., Hussain, Z., Osman, M.K.: Intelligent medical disease diagnosis using improved hybrid genetic algorithm–multilayer perceptron network. J. Med. Syst. 37, 9934 (2013)CrossRefGoogle Scholar
  9. 9.
    Maulik, U., Chakraborty, D.: Fuzzy preference based feature selection and semisupervised SVM for cancer classification. IEEE Trans. Nanobioscience. 13, 152–160 (2014)CrossRefGoogle Scholar
  10. 10.
    Yassi, M., Moattar, M.H.: Robust and stable feature selection by integrating ranking methods and wrapper technique in genetic data classification. Biochem. Biophys. Res. Commun. 446, 850–856 (2014)CrossRefGoogle Scholar
  11. 11.
    Inza, I., Larrañaga, P., Blanco, R., Cerrolaza, A.J.: Filter versus wrapper gene selection approaches in DNA microarray domains. Artif. Intell. Med. 31, 91–103 (2004)CrossRefGoogle Scholar
  12. 12.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995)zbMATHGoogle Scholar
  13. 13.
    Fradkov, A.L., Evans, R.J.: Control of chaos: methods and applications in engineering. Annu. Rev. Control 29, 33–56 (2005)CrossRefGoogle Scholar
  14. 14.
    Doherty, M.F., Ottino, J.M.: Chaos in deterministic systems: strange attractors, turbulence, and applications in chemical engineering. Chem. Eng. Sci. 43, 139–183 (1988)CrossRefGoogle Scholar
  15. 15.
    Skinner, J.E., Molnar, M., Vybiral, T., Mitra, M.: Application of chaos theory to biology and medicine. Integr. Physiol. Behav. Sci. 27, 39–53 (1992)CrossRefGoogle Scholar
  16. 16.
    Golub, T.R.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science (80–) 286, 531–537 (1999)Google Scholar
  17. 17.
    West, M., Blanchette, C., Dressman, H., Huang, E., Ishida, S., Spang, R., Zuzan, H., Olson, J.A., Marks, J.R., Nevins, J.R.: Predicting the clinical status of human breast cancer by using gene expression profiles. Proc. Natl. Acad. Sci. USA 98, 11462–11467 (2001)CrossRefGoogle Scholar
  18. 18.
    Shevade, S.K., Keerthi, S.S.: A simple and efficient algorithm for gene selection using sparse logistic regression. Bioinformatics 19, 2246–2253 (2003)CrossRefGoogle Scholar
  19. 19.
    Alon, U., Barkai, N., Notterman, D.A., Gish, K., Ybarra, S., Mack, D., Levine, A.J.: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Natl. Acad. Sci. 96, 6745–6750 (1999)CrossRefGoogle Scholar
  20. 20.
    Bache, K., Lichman, M.: {UCI} machine learning repository. (2013)
  21. 21.
    Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)Google Scholar
  22. 22.
    Dorigo, M., Stützle, T.: Ant colony optimization: overview and recent advances. In: Gendreau, M., Potvin, J.-Y. (eds.) Handbook of Metaheuristics SE—8, pp. 227–263. Springer, New York (2010)CrossRefGoogle Scholar
  23. 23.
    Shuai, R., Jing, W., Zhang, X.: Research on chaos partheno-genetic algorithm for TSP. In: 2010 International Conference on Computer Application and System Modeling (ICCASM 2010), pp. V1–290–V1–293. IEEE (2010)Google Scholar
  24. 24.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software. ACM SIGKDD Explor. Newslett. 11, 10 (2009)CrossRefGoogle Scholar
  25. 25.
    MATLAB: version 7.10.0 (R2010a). The MathWorks Inc., Natick, Massachusetts (2010)Google Scholar
  26. 26.
    Chang, C.-C., Lin, C.-J.: LIBSVM. ACM Trans. Intell. Syst. Technol. 2, 1–27 (2011)CrossRefGoogle Scholar
  27. 27.
    Ding, C., Peng, H.: Minimum redundancy feature selection from microarray gene expression data. J. Bioinform. Comput. Biol. 03, 185–205 (2005)CrossRefGoogle Scholar
  28. 28.
    Krishnapuram, B., Carin, L., Hartemink, A.: 1 Gene expression analysis: joint feature selection and classifier design. Kernel Methods Comput. Biol. 299–317 (2004)Google Scholar
  29. 29.
    Bascil, M.S., Oztekin, H.: A study on hepatitis disease diagnosis using probabilistic neural network. J. Med. Syst. 36, 1603–1606 (2012)CrossRefGoogle Scholar
  30. 30.
    Bascil, M.S., Temurtas, F.: A study on hepatitis disease diagnosis using multilayer neural network with levenberg marquardt training algorithm. J. Med. Syst. 35, 433–436 (2011)CrossRefGoogle Scholar
  31. 31.
    Afif, M.H., Hedar, A.-R., Hamid, T.H.A., Mahdy, Y.B.: SS-SVM (3SVM): a new classification method for hepatitis disease diagnosis. Int. J. Adv. Comput. Sci. Appl. 4 (2013)Google Scholar
  32. 32.
    Li, S., Wu, X., Hu, X.: Gene selection using genetic algorithm and support vectors machines. Soft. Comput. 12, 693–698 (2008)CrossRefGoogle Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  • Gunjan Mishra
    • 1
  • Vivek Ananth
    • 1
  • Kalpesh Shelke
    • 2
  • Deepak Sehgal
    • 1
  • Jayaraman Valadi
    • 1
  1. 1.Shiv Nadar UniversityGautam Budha NagarIndia
  2. 2.Centre for Modeling and SimulationPune UniversityPuneIndia

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