Journal of Computer Sciences and Applications
ISSN (Print): 2328-7268 ISSN (Online): 2328-725X Website: http://www.sciepub.com/journal/jcsa Editor-in-chief: Minhua Ma, Patricia Goncalves
Open Access
Journal Browser
Go
Journal of Computer Sciences and Applications. 2015, 3(6), 181-184
DOI: 10.12691/jcsa-3-6-14
Open AccessArticle

Comparison of Kernel Selection for Support Vector Machines Using Diabetes Dataset

Tapas Ranjan Baitharu1, Subhendu Ku. Pani1, 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

Pub. Date: January 04, 2016

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

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]  Klosgen W and Zytkow J M (eds.), Handbook of data mining and knowledge discovery, OUP, Oxford, 2002.
 
[2]  Provost, F., & Fawcett, T., Robust Classification for Imprecise Environments. Machine Learning,Vol.42, No.3, pp. 203-231,2001.
 
[3]  Larose D T, Discovering knowledge in data: an introduction to data mining, John Wiley, New York, 2005.
 
[4]  Kantardzic M, Data mining: concepts, models, methods, and algorithms, John Wiley, New Jersey, 2003.
 
[5]  Goldschmidt P S, Compliance monitoring for anomaly detection, Patent no. US 6983266 B1, issue date January3, 2006, Available at: www.freepatentsonline.com/6983266.html.
 
[6]  Bace R, Intrusion Detection, Macmillan Technical Publishing, 2000.
 
[7]  A. B. M. S. Ali, and A. Abraham, “An Empirical Comparison of Kernel Selection for Support Vector Machines”, Soft computing systems: design, management and applications, Ajith Abraham, Javier Ruiz-del-Solar,Mario Köppen (eds.), IOS Press Publisher, Amsterdam, 2002, pp. 321-330.
 
[8]  Agrawal R. and Srikant R. Fast Algorithms for Mining Association Rules. In M. Jarke J. Bocca and C. Zaniolo, editors, Proceeedings of the 20th International Conference on Very Large Data Bases (VLDB’94), pages 475-486, Santiago de Chile, Chile, Sept 1994. Morgan Kaufmann.
 
[9]  Scheffer T. Finding Association Rules That Trade Support Optimally against Confidence. Unpublished manuscript.
 
[10]  M. Bak, “Support Vector Classifier with Linguistic Interpretation of the Kernel Matrix in Speaker Verification”, Man-Machine Interactions, Krzysztof A. Cyran, Stanislaw Kozielski, James F. Peters (eds.), ISSN 1867-5662, Springer, 2009, pp 399-406.
 
[11]  UCI Machine Learning Repository, Available at http://archive.ics.uci.edu/ml/machine-learningdatabases/statlog/german/.
 
[12]  C. J. C. Burges and B. Schölkopf, “Improving the Accuracy and Speed of Support Vector Learning Machines”, Advances in Neural Information Processing Systems 9, Cambridge, MIT Press, 1997, pp. 375-381.
 
[13]  Berry M J A and Linoff G S, Data mining techniques: for marketing, sales, and relationship management, 2nded (John Wiley; New York), 2004.
 
[14]  M. Bentoumi, G. Millerioux, G. Bloch, L. Oukhellou and P. Aknin, “Classification de Défauts de Rail par SVM,” Congrès International IEEE de Signaux, Circuits et Systèmes SCS’04, Tunisie, 2004, pp. 242-245.
 
[15]  Fuchs G, Data Mining: if only it really were about Beer and Diapers, Information Management Online, July 1, (2004), Available at: http://www.informationmanagement. com/ news/10061331.html.