Skip to main content
SpringerLink
Log in

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

  • Conference paper
  • First Online:
Proceedings of Fourth International Conference on Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 336))

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Baek, S., Tsai, C.-A., Chen, J.J.: Development of biomarker classifiers from high-dimensional data. Brief. Bioinform. 10, 537–546 (2009)

    Article  Google Scholar 

  2. Poncelet, P., Masseglia, F., Teisseire, M.: Successes and New Directions in Data Mining. IGI Global, Hershey (2008)

    Google Scholar 

  3. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)

    MATH  Google Scholar 

  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. Saeys, Y., Inza, I., Larrañaga, P.: A review of feature selection techniques in bioinformatics. Bioinformatics 23, 2507–2517 (2007)

    Article  Google Scholar 

  6. Chrysostomou, K.: Encyclopedia of Data Warehousing and Mining, 2nd edn. IGI Global, Hershey (2008)

    Google Scholar 

  7. Liu, H., Motoda, H. (eds.): Feature Extraction, Construction and Selection. Springer, Boston (1998)

    MATH  Google Scholar 

  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)

    Article  Google Scholar 

  9. Maulik, U., Chakraborty, D.: Fuzzy preference based feature selection and semisupervised SVM for cancer classification. IEEE Trans. Nanobioscience. 13, 152–160 (2014)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  12. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995)

    MATH  Google Scholar 

  13. Fradkov, A.L., Evans, R.J.: Control of chaos: methods and applications in engineering. Annu. Rev. Control 29, 33–56 (2005)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  18. Shevade, S.K., Keerthi, S.S.: A simple and efficient algorithm for gene selection using sparse logistic regression. Bioinformatics 19, 2246–2253 (2003)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  20. Bache, K., Lichman, M.: {UCI} machine learning repository. http://archive.ics.uci.edu/ml (2013)

  21. Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)

    Google Scholar 

  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)

    Chapter  Google Scholar 

  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. 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)

    Article  Google Scholar 

  25. MATLAB: version 7.10.0 (R2010a). The MathWorks Inc., Natick, Massachusetts (2010)

    Google Scholar 

  26. Chang, C.-C., Lin, C.-J.: LIBSVM. ACM Trans. Intell. Syst. Technol. 2, 1–27 (2011)

    Article  Google Scholar 

  27. Ding, C., Peng, H.: Minimum redundancy feature selection from microarray gene expression data. J. Bioinform. Comput. Biol. 03, 185–205 (2005)

    Article  Google Scholar 

  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. Bascil, M.S., Oztekin, H.: A study on hepatitis disease diagnosis using probabilistic neural network. J. Med. Syst. 36, 1603–1606 (2012)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. Li, S., Wu, X., Hu, X.: Gene selection using genetic algorithm and support vectors machines. Soft. Comput. 12, 693–698 (2008)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Shiv Nadar University, Gautam Budha Nagar, 201314, Uttar Pradesh, India

    Gunjan Mishra, Vivek Ananth, Deepak Sehgal & Jayaraman Valadi

  2. Centre for Modeling and Simulation, Pune University, Pune, 4110001, India

    Kalpesh Shelke

Authors
  1. Gunjan Mishra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vivek Ananth
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kalpesh Shelke
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Deepak Sehgal
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jayaraman Valadi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jayaraman Valadi .

Editor information

Editors and Affiliations

  1. Mathematics, National Institute of Technology Silchar, Silchar, Assam, India

    Kedar Nath Das

  2. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Kusum Deep

  3. Department of Paper Technology, Indian Institute of Technology Roorkee, Roorkee, India

    Millie Pant

  4. South Asian University, New Delhi, India

    Jagdish Chand Bansal

  5. Department of Computer Science, Liverpool Hope University, Liverpool, United Kingdom

    Atulya Nagar

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer India

About this paper

Cite this paper

Mishra, G., Ananth, V., Shelke, K., Sehgal, D., Valadi, J. (2015). Hybrid ACO Chaos-Assisted Support Vector Machines for Classification of Medical Datasets. In: Das, K., Deep, K., Pant, M., Bansal, J., Nagar, A. (eds) Proceedings of Fourth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 336. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2220-0_8

Download citation

  • DOI: https://doi.org/10.1007/978-81-322-2220-0_8

  • Published:

  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2219-4

  • Online ISBN: 978-81-322-2220-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation