Centre for Modeling and Simulation
Savitribai Phule Pune University All Models Are False, Some Are Useful

Technical Report CMS-TR-20150210


Title Hybrid ACO Chaos-Assisted Support Vector Machines for Classification of Medical Datasets
Author/s Gunjan Mishra, Vivek Ananth
Shiv Nadar University, Gautam Budha Nagar, 201314, Uttar Pradesh, India


Kalpesh Shelke
Centre for Modeling and Simulation, Savitribai Phule Pune University, Pune 411 007 India


Deepak Sehgal, and Jayaraman V.K.
Shiv Nadar University, Gautam Budha Nagar, 201314, Uttar Pradesh, India
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.
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Citing This Document Gunjan Mishra, Vivek Ananth, Kalpesh Shelke, Deepak Sehgal, and V.K. Jayaraman , Hybrid ACO Chaos-Assisted Support Vector Machines for Classification of Medical Datasets . Technical Report CMS-TR-20150210 of the Centre for Modeling and Simulation, Savitribai Phule Pune University, Pune 411007, India (2015); available at http://cms.unipune.ac.in/reports/.
Notes, Published Reference, Etc. Published in the Proceedings of Fourth International Conference on Soft Computing for Problem Solving: Advances in Intelligent Systems and Computing Volume 336, 2015, pp 91-101
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