International Journal of Advanced and Applied Sciences

Int. j. adv. appl. sci.

EISSN: 2313-3724

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

Full Text - PDF          XML


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 (

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


  1. Begum S, Chakraborty D, and Sarkar R (2015). Data classification using feature selection and kNN machine learning approach. In the International Conference on Computational Intelligence and Communication Networks, IEEE, Jabalpur, India: 811-814. 
  2. Bratko I, Michalski RS, and Kubat M (1999). Machine learning and data mining: methods and applications. John Wiley and Sons, Hoboken, USA.     
  3. Cortes C and Vapnik V (1995). Support-vector networks. Machine Learning, 20(3): 273-297. 
  4. Deepajothi S and Selvarajan S (2012). A comparative study of classification techniques on adult data set. International Journal of Engineering Research and Technology, 1(8): 1-8.     
  5. Deniz A, Kiziloz HE, Dokeroglu T, and Cosar A (2017). Robust multiobjective evolutionary feature subset selection algorithm for binary classification using machine learning techniques. Neurocomputing, 241: 128-146. 
  6. Giron-Sierra JM (2017). Data analysis and classification. In: Giron-Sierra JM (Ed.), Digital signal processing with matlab examples: 647-835, Springer Singapore, India. 
  7. Mehenni T (2015). Integration of useful links in distributed databases using decision tree classification. In the 6th International Conference on Information Systems and Economic Intelligence, IEEE, Hammamet, Tunisia: 5-9. 
  8. Mohana S, Sahaaya SA, and Mary A (2016). A comparitive framework for feature selction in privacy preserving data mining techniques using pso and k-anonumization. Iioab Journal, 7(9): 804-811.     
  9. Nasridinov A, Ihm SY, and Park YH (2013). A decision tree-based classification model for crime prediction. In Information Technology Convergence, Springer, Dordrecht, Netherlands: 531-538. 
  10. Nowak BA, Nowicki RK, and Mleczko WK (2013), A new method of improving classification accuracy of decision tree in case of incomplete samples. In the International Conference on Artificial Intelligence and Soft Computing, Springer Berlin Heidelberg, Heidelberg, Germany: 448-458. 
  11. Prakash VA, Ashoka DV, and Aradya VM (2015). Application of data mining techniques for defect detection and classification. In the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications, Springer International Publishing: 387-395. 
  12. Quinlan JR (1986). Induction of decision trees. Machine Learning, 1(1): 81-106. 
  13. Salleh MNM (2014). Improving weighted fuzzy decision tree for uncertain data classification. In: Herawan T, Ghazali R, and Deris MM (Eds.), Recent advances on soft computing and data mining: 249-259. Springer International Publishing, Berlin, Germany.     
  14. Song J, Zhu Z, Scully P, and Price C (2013). Selecting features for anomaly intrusion detection: A novel method using fuzzy C means and decision tree classification. In: Wang G, Ray I, Feng D, and Rajarajan M (Eds.), Cyberspace Safety and Security: 299-307. Springer International Publishing, Berlin, Germany. 
  15. Sun Y, Kamel M, and Wong A (2005). Empirical study on weighted voting multiple classifiers. In: Singh S, Singh M, Apte C, and Perner P (Eds.), Pattern recognition and data mining: 335-344. Springer, Berlin, Germany. 
  16. Vijayan A, Kareem S, and Kizhakkethottam JJ (2016). Face recognition across gender transformation using SVM classifier. Procedia Technology, 24: 1366-1373.