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

Part of the Advances in Intelligent Systems and Computing book series (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.

Keywords

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

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Correspondence to Jayaraman Valadi .

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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

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

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