Comparison of Kernel Selection for Support Vector Machines Using Diabetes Dataset

Tapas Ranjan Baitharu¹, Subhendu Ku. Pani^1, and Sunil kumar Dhal²

¹Department of Computer Science & Engineering, Orissa Engineering College, Bhubaneswar, Odisha (India) under Biju Patnaik University of Technology, Odisha

²Department of Computer Science & Engineering, SriSri University, Bhubaneswar, Odisha, India

Cite this paper:
Tapas Ranjan Baitharu, Subhendu Ku. Pani and Sunil kumar Dhal. Comparison of Kernel Selection for Support Vector Machines Using Diabetes Dataset. Journal of Computer Sciences and Applications. 2015; 3(6):181-184. doi: 10.12691/jcsa-3-6-14

Abstract

One of the major problems in the study of Support vector machine (SVM) is kernel selection, that’s based necessarily on the problem of deciding a kernel function for a particular task and dataset. By contradiction to other machine learning algorithms, SVM focuses on maximizing the generalisation ability, which depends on the empirical risk and the complexity of the machine. We were focused on SVM trained using linear, polynomial, puk and Radial Basic Function (RBF) kernels. A preliminary study has been made between SVM using the best choice of kernel. Results had revealed that SVM trained using Linear Kernel is the best choice for dealing with Diabetes dataset.

Keywords:
data mining machine learning algorithms support vector machine kernels function

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