International journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN:2313-626X

Frequency: 12
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 Volume 5, Issue 9 (September 2018), Pages: 33-38

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 Original Research Paper

 Title: Performance analysis of support vector machine based classifiers

 Author(s): Zulfiqar Ali 1, 2, * , Syed Khuram Shahzad 3, Waseem Shahzad 2

 Affiliation(s):

 1Department of Computer Science and Information Technology, University of Lahore, Lahore, Pakistan
 2Department of Computer Science, National University of Computer and Emerging Science, Islamabad, Pakistan
 3Department of Computer Science and Information Technology, The Superior College, Lahore, Pakistan

 https://doi.org/10.21833/ijaas.2018.09.007

 Full Text - PDF          XML

 Abstract:

Classification is a challenging problem in the various fields of knowledge i.e., Pattern Recognition, Data Mining, Knowledge Discovery from Database etc. There is various classification methods are proposed in the contemporary literature. The choice of an appropriate classifier to achieve the optimal performance on a specific problem needs more empirical studies. There are various algorithmic paradigms like, Associative Classification; Decision Trees based classification, Statistical Classification and Support Vector Machines etc. which are exploited for the classification purposes. This paper investigates the performance of Support Vector Machine (SVM) based classifiers namely SMO-C, C-SVM-C, and NU-SVM-C. SVM is a very successful classification approach for the binary classification as well as non-binary classification problems. This study, performance comparative analysis of SVM based classification approach on public data sets; exploit the implementation of the corresponding classifiers in the KEEL. The SVM-C approach wins one time, draw 5 times and lost 6 times with respective other approaches. The NU_SVM-C win one time, draw 4 times and lost 7 times while SMO-C wins 5 times, draw 3 times and loss 4 times. It is shown that the performance of SMO-C is promising with respect to other SVM based classifiers. 

 © 2018 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Classification, Support vector machine, KEEL, SVM kernel

 Article History: Received 28 March 2018, Received in revised form 10 July 2018, Accepted 12 July 2018

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2018.09.007

 Citation:

 Ali Z , Shahzad SK, and Shahzad W (2018). Performance analysis of support vector machine based classifiers. International Journal of Advanced and Applied Sciences, 5(9): 33-38

 Permanent Link:

 http://www.science-gate.com/IJAAS/2018/V5I9/Ali.html

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