International Journal of Advanced and Applied Sciences
Int. j. adv. appl. sci.
Print ISSN: 2313-626X
Volume 4, Issue 9 (September 2017), Pages: 138-143
Title: A novel hybrid support vector machine with decision tree for data classification
Author(s): Mehdi Zekriyapanah Gashti *
Department of Computer Engineering, Payame Noor University, Tehran, Iran
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The purpose of this paper is to increase the accuracy of a proposed support vector machine model using hybrid model of SVM and ID3. Then the hybrid approach based on SVM and ID3 tree will be evaluated focusing on analyzing the impact of ID3 on SVM performance. The evaluation process was carried out on the global dataset and Adult reference extracted from KEEL dataset repository. The obtained results demonstrate higher classification accuracy (0.9125) of the proposed model compared to SVM and ID3.
© 2017 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, Decision tree
Article History: Received 18 March 2017, Received in revised form 20 May 2017, Accepted 5 July 2017
Digital Object Identifier:
Gashti MZ (2017). A novel hybrid support vector machine with decision tree for data classification. International Journal of Advanced and Applied Sciences, 4(9): 138-143
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