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
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Baek, S., Tsai, C.-A., Chen, J.J.: Development of biomarker classifiers from high-dimensional data. Brief. Bioinform. 10, 537–546 (2009)
Poncelet, P., Masseglia, F., Teisseire, M.: Successes and New Directions in Data Mining. IGI Global, Hershey (2008)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)
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)
Saeys, Y., Inza, I., Larrañaga, P.: A review of feature selection techniques in bioinformatics. Bioinformatics 23, 2507–2517 (2007)
Chrysostomou, K.: Encyclopedia of Data Warehousing and Mining, 2nd edn. IGI Global, Hershey (2008)
Liu, H., Motoda, H. (eds.): Feature Extraction, Construction and Selection. Springer, Boston (1998)
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)
Maulik, U., Chakraborty, D.: Fuzzy preference based feature selection and semisupervised SVM for cancer classification. IEEE Trans. Nanobioscience. 13, 152–160 (2014)
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)
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)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995)
Fradkov, A.L., Evans, R.J.: Control of chaos: methods and applications in engineering. Annu. Rev. Control 29, 33–56 (2005)
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)
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)
Golub, T.R.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science (80–) 286, 531–537 (1999)
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)
Shevade, S.K., Keerthi, S.S.: A simple and efficient algorithm for gene selection using sparse logistic regression. Bioinformatics 19, 2246–2253 (2003)
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)
Bache, K., Lichman, M.: {UCI} machine learning repository. http://archive.ics.uci.edu/ml (2013)
Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)
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)
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)
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)
MATLAB: version 7.10.0 (R2010a). The MathWorks Inc., Natick, Massachusetts (2010)
Chang, C.-C., Lin, C.-J.: LIBSVM. ACM Trans. Intell. Syst. Technol. 2, 1–27 (2011)
Ding, C., Peng, H.: Minimum redundancy feature selection from microarray gene expression data. J. Bioinform. Comput. Biol. 03, 185–205 (2005)
Krishnapuram, B., Carin, L., Hartemink, A.: 1 Gene expression analysis: joint feature selection and classifier design. Kernel Methods Comput. Biol. 299–317 (2004)
Bascil, M.S., Oztekin, H.: A study on hepatitis disease diagnosis using probabilistic neural network. J. Med. Syst. 36, 1603–1606 (2012)
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)
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)
Li, S., Wu, X., Hu, X.: Gene selection using genetic algorithm and support vectors machines. Soft. Comput. 12, 693–698 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights 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)