An Analysis on Performance of Decision Tree Algorithms using Student‟s Qualitative Data

Full Text (PDF, 595KB), PP.18-27

Views: 0 Downloads: 0


T. Miranda Lakshmi 1,* A. Martin 2 R.Mumtaj Begum 3 V. Prasanna Venkatesan 4

1. Department of Computer Science, Bharathiyar University, Coimbatore, India

2. Department of Banking Technology, Pondicherry University, Pondicherry, India

3. Department of Computer Science, Krishnasamy college, Cuddalore, India

4. Department of Banking Technology,Pondicherry University, Pondicherry , India

* Corresponding author.


Received: 23 Jan. 2013 / Revised: 10 Mar. 2013 / Accepted: 2 Apr. 2013 / Published: 8 May 2013

Index Terms

Decision Tree Algorithm, ID3, C4.5, CART, student‟s qualitative data.


Decision Tree is the most widely applied supervised classification technique. The learning and classification steps of decision tree induction are simple and fast and it can be applied to any domain. In this research student qualitative data has been taken from educational data mining and the performance analysis of the decision tree algorithm ID3, C4.5 and CART are compared. The comparison result shows that the Gini Index of CART influence information Gain Ratio of ID3 and C4.5. The classification accuracy of CART is higher when compared to ID3 and C4.5. However the difference in classification accuracy between the decision tree algorithms is not considerably higher. The experimental results of decision tree indicate that student’s performance also influenced by qualitative factors.

Cite This Paper

T.Miranda Lakshmi, A.Martin, R.Mumtaj Begum, V.Prasanna Venkatesan, "An Analysis on Performance of Decision Tree Algorithms using Student’s Qualitative Data", International Journal of Modern Education and Computer Science (IJMECS), vol.5, no.5, pp.18-27, 2013. DOI:10.5815/ijmecs.2013.05.03


[1]Pornnapadol, "Children who have learning disabilities", Child and Adolescent Psychiatric Bulletin Club of Thailand, October-December, 2004, pp.47-48.
[2]B.Nithyassik, Nandhini, Dr.E.Chandra, “Classification Techniques in Education Domain”, International Journal on Computer Science and Engineering, 2010, Vol. 2, No.5, pp.1647-1684.
[3]Jiawei Han and Micheline Kamber, "Data Mining: Concepts and Techniques", 2nd ed., Morgan Kaufmann Publishers, 2006.
[4]M. El-Halees, "Mining Student Data to Analyze Learning Behavior: A Case Study". In Proceedings of the 2008 International Arab Conference of Information Technology (ACIT2008), University of Sfax, Tunisia, Dec 15- 18
[5]U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R.Uthurusamy, “Advances in Knowledge Discovery and Data Mining”, AAAI/MIT Press, 1996.
[6]Aman Kumar Sharma, Suruchi Sahni, ” A Comparative Study of Classification Algorithms for Spam Email Data Analysis”, International Journal on Computer Science and Engineering, May 2011 ,Vol. 3 No. 5 ,pp 1890-1895.
[7]Nguyen Thai Nghe; Janecek, P.; Haddawy, P., "A comparative analysis of techniques for predicting academic performance”, Frontiers In Education Conference - Global Engineering: Knowledge Without Borders, Opportunities Without Passports, 2007. FIE '07. 37th Annual , pp.T2G-7,T2G-12, Oct. 2007
[8]S.Wongpun, A. Srivihok, "Comparison of attribute selection techniques and algorithms in classifying bad behaviors of vocational education students", Digital Ecosystems and Technologies,2nd IEEE International Conference on , pp.526,531, Feb. 2008
[9]B.K Bharadwaj, S.Pal, “Mining Educational data to Analyze student’s performance” , International Journal of Computer Science and Applications, 2011, Vol.2, No.6, pp 63-69.
[10]A.L.Radaideh, Q.A.AI-Shawakfa, E.M. AI-Najjar, “Mining student data using Decision Tree” International Arab Conference on Information Technology (ACIT 2006),Yarmouk University, 2006.
[11]Sunita B.Aher, L.M.R.J. lobo, “A Comparative study of classification algorithms”, International Journal of Information Technology and Knowledge Management, July-December 2012, Volume 5, N0.2, pp 239-243.
[12]V.P Bresfelean, “Analysis and predictions on student’s behavior using decision trees in WEKA environment”, Proceedings of the ITI 2007 29thInternational Conference on Information Technology Interfaces, 2007 , June 25-28 .
[13]Z.J.Kovacic.; "Early prediction of student success: Mining student enrollment data", Proceedings of Informing Science and IT Educational Conference, 2010,pp 648-665.
[14]A Bellaachia, E Guven, “Predicting the student performance using Data Mining Techniques”, International Journal of Computer Applications, 2006, Vol.6.
[15]Margret H. Dunham, “Data Mining: Introductory and advance topic”, Pearson Education India, 2006.
[16]J.R.Quinlan, “Induction of Decision Tree”, Journal of Machine learning”, Morgan Kaufmann Vol.1, 1986, pp.81-106.
[17]J.R.Quinlan, “C4.5: Programs for Machine Learning”, Morgan Kaufmann Publishers, Inc, 1992.
[18]J. Quinlan, “Learning decision tree classifiers”. ACM Computing Surveys (CSUR), 28(1):71–72, 1996.
[19]WEKA, University of Waikato, New Zealand, (accessed July 18 , 2012)
[20]B.k. Bhardwaj, S.PAL,” Data Mining: A prediction for performance improvement using classification”, International journal of Computer Science and Information Security, April 2011, Vol.9, No.4, pp 136-140.
[21]Surjeet Kumar Yadav, Brijesh Bharadwaj, Saurabh Pal, “A Data Mining Application: A Comparative study for predicting student’s performance”, International Journal of Innovative Technology and Creative Engineering, Vol.1 No.12 (2011) 13-19
[22]G Stasis, A.C. Loukis, E.N. Pavlopoulos, S.A. Koutsouris, “Using decision tree Algorithms as a basis for a heart sound diagnosis decision support system”, Information Technology Applications in Biomedicine, 4th International IEEE EMBS Special Topic Conference, April 2003.
[23]M.J. Berry, G.S. Linoff, “Data Mining Techniques: For Marketing Sales and Customer Relationship Management”, Wiley Publishing, 2004.
[24]S.Anupama Kumar, M.N.Vijayalakshmi, "Prediction of the students recital using classification technique ", International journal of computing , Volume 1, Issue 3 July 2011,pp305-309.