An Analysis on Performance of Decision Tree Algorithms using 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.

[1]  Aman Kumar Sharma,et al.  A Comparative Study of Classification Algorithms for Spam Email Data Analysis , 2011 .

[2]  Michael J. A. Berry,et al.  Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management , 2004 .

[3]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[4]  Euripidis N. Loukis,et al.  Using decision tree algorithms as a basis for a heart sound diagnosis decision support system , 2003, 4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine, 2003..

[5]  Surjeet Kumar Yadav,et al.  Data Mining Applications: A comparative Study for Predicting Student's performance , 2012, ArXiv.

[6]  K. Nandhini,et al.  CLASSIFICATION TECHNIQES IN EDUCATION DOMAIN , 2010 .

[7]  Nguyen Thai Nghe,et al.  A comparative analysis of techniques for predicting academic performance , 2007, 2007 37th Annual Frontiers In Education Conference - Global Engineering: Knowledge Without Borders, Opportunities Without Passports.

[8]  Yu Guo,et al.  Sample size and statistical power considerations in high-dimensionality data settings: a comparative study of classification algorithms , 2010, BMC Bioinformatics.

[9]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[10]  Qasem A. Al-Radaideh,et al.  Mining Student Data Using Decision Trees , 2006 .

[11]  A. Srivihok,et al.  Comparison of attribute selection techniques and algorithms in classifying bad behaviors of vocational education students , 2008, 2008 2nd IEEE International Conference on Digital Ecosystems and Technologies.

[12]  Saurabh Pal,et al.  Mining Educational Data to Analyze Students' Performance , 2012, ArXiv.

[13]  Zlatko J. Kovacic,et al.  Early Prediction of Student Success: Mining Students Enrolment Data , 2010 .

[14]  V.P. Bresfelean,et al.  Analysis and Predictions on Students' Behavior Using Decision Trees in Weka Environment , 2007, 2007 29th International Conference on Information Technology Interfaces.

[15]  Margaret H. Dunham,et al.  Data Mining: Introductory and Advanced Topics , 2002 .

[16]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[17]  Alaa M. El-Halees Mining students data to analyze e-Learning behavior: A Case Study , 2009 .

[18]  J. Ross Quinlan,et al.  Learning decision tree classifiers , 1996, CSUR.

[19]  Saurabh Pal,et al.  Data Mining: A prediction for performance improvement using classification , 2012, ArXiv.

[20]  Gregory Piatetsky-Shapiro,et al.  Advances in Knowledge Discovery and Data Mining , 2004, Lecture Notes in Computer Science.

[21]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .