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Reseach Article

Comparative Analysis of Random Forest, REP Tree and J48 Classifiers for Credit Risk Prediction

Published on March 2015 by Lakshmi Devasena C
International Conference on Communication, Computing and Information Technology
Foundation of Computer Science USA
ICCCMIT2014 - Number 3
March 2015
Authors: Lakshmi Devasena C
d034c088-f735-44fc-9aa0-3a5afdb74617

Lakshmi Devasena C . Comparative Analysis of Random Forest, REP Tree and J48 Classifiers for Credit Risk Prediction. International Conference on Communication, Computing and Information Technology. ICCCMIT2014, 3 (March 2015), 30-36.

@article{
author = { Lakshmi Devasena C },
title = { Comparative Analysis of Random Forest, REP Tree and J48 Classifiers for Credit Risk Prediction },
journal = { International Conference on Communication, Computing and Information Technology },
issue_date = { March 2015 },
volume = { ICCCMIT2014 },
number = { 3 },
month = { March },
year = { 2015 },
issn = 0975-8887,
pages = { 30-36 },
numpages = 7,
url = { /proceedings/icccmit2014/number3/19785-7033/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Communication, Computing and Information Technology
%A Lakshmi Devasena C
%T Comparative Analysis of Random Forest, REP Tree and J48 Classifiers for Credit Risk Prediction
%J International Conference on Communication, Computing and Information Technology
%@ 0975-8887
%V ICCCMIT2014
%N 3
%P 30-36
%D 2015
%I International Journal of Computer Applications
Abstract

Envisaging the Credit nonpayer is a risky task of Financial Industries like Banks. find out the defaulter before giving loan is a noteworthy and conflict-ridden task of the Bankers. Classification techniques are the superior choice for predictive analysis like finding the claimant, whether he/she is a modest customer or a cheat. Defining the excellent classifier is a tough assignment for any industrialist like a banker. This gives consent to computer science researchers to drill down efficient research works through evaluating different classifiers and finding out the best classifier for such predictive problems. This research work scrutinizes the efficiency of different Tree Based Classifiers (Random Forest, REP Tree and J48 Classifiers) for the credit risk prediction and compares their robustness through various measures. German credit dataset has been taken and used to envisage the credit risk with the help of open source machine learning tool.

References
  1. Germano C. Vasconcelos, Paulo J. L. Adeodato and Domingos S. M. P. Monteiro. 1999. A Neural Network Based Solution for the Credit Risk Assessment Problem. Proceedings of the IV Brazilian Conference on Neural Networks - IV Congresso Brasileiro de Redes Neurais, (July 1999), 269-274.
  2. Tian-Shyug Lee, Chih-Chou Chiu, Chi-Jie Lu and I-Fei Chen. 2002. Credit scoring using the hybrid neural discriminant technique. Expert Systems with Applications (Elsevier) 23, 245–254.
  3. Zan Huang, Hsinchun Chena, Chia-Jung Hsu, Wun-Hwa Chen and Soushan Wu. 2004. Credit rating analysis with support vector machines and neural networks: a market comparative study," Decision Support Systems (Elsevier) 37, 543– 558.
  4. Kin Keung Lai, Lean Yu, Shouyang Wang, and Ligang Zhou. 2006. Credit Risk Analysis Using a Reliability-Based Neural Network Ensemble Model. S. Kollias et al. (Eds. ): ICANN 2006, Part II, Springer LNCS 4132, 682 – 690.
  5. Eliana Angelini, Giacomo di Tollo, and Andrea Roli. 2006. A Neural Network Approach for Credit Risk Evaluation," Kluwer Academic Publishers, 1 – 22.
  6. S. Kotsiantis. 2007. Credit risk analysis using a hybrid data mining model. Int. J. Intelligent Systems Technologies and Applications, Vol. 2, No. 4, 345 – 356.
  7. Hamadi Matoussi and Aida Krichene. 2007. Credit risk assessment using Multilayer Neural Network Models - Case of a Tunisian bank.
  8. Lean Yu, Shouyang Wang, and Kin Keung Lai. 2008. Credit risk assessment with a multistage neural network ensemble learning approach. Expert Systems with Applications (Elsevier) 34, pp. 1434–1444.
  9. Arnar Ingi Einarsson. 2008. Credit Risk Modeling. Ph. D Thesis, Technical University of Denmark.
  10. Sanaz Pourdarab, Ahmad Nadali and Hamid Eslami Nosratabadi. 2011. A Hybrid Method for Credit Risk Assessment of Bank Customers. International Journal of Trade, Economics and Finance, Vol. 2, No. 2, (April 2011).
  11. Vincenzo Pacelli and Michele Azzollini. 2011. An Artificial Neural Network Approach for Credit Risk Management. Journal of Intelligent Learning Systems and Applications, 3, 103-112.
  12. A. R. Ghatge and P. P. Halkarnikar. Ensemble Neural Network Strategy for Predicting Credit Default Evaluation. International Journal of Engineering and Innovative Technology (IJEIT), Volume 2, Issue 7, (January 2013), 223 – 225.
  13. Lakshmi Devasena, C. 2014. Adeptness Evaluation of Memory Based Classifiers for Credit Risk Analysis. Proc. of International Conference on Intelligent Computing Applications - ICICA 2014, 978-1-4799-3966-4/14 (IEEE Explore), 6-7 March 2014, 143-147.
  14. Lakshmi Devasena, C. 2014. Adeptness Comparison between Instance Based and K Star Classifiers for Credit Risk Scrutiny. International Journal of Innovative Research in Computer and Communication Engineering, Vol. 2, Special Issue 1, (March 2014).
  15. Lakshmi Devasena, C. 2014. Effectiveness Assessment between Sequential Minimal Optimization and Logistic Classifiers for Credit Risk Prediction. International Journal of Application or Innovation in Engineering & Management, Volume3, Issue 4, (April 2014), 55 - 63.
  16. Lakshmi Devasena, C. 2014. Efficiency Comparison of Multilayer Perceptron and SMO Classifier for Credit Risk Prediction. International Journal of Advanced Research in Computer and Communication Engineering, Vol. 3, Issue 4, 6156 - 6162.
  17. Lakshmi Devasena, C. 2014. Competency Assessment between JRip and Partial Decision Tree Classifiers for Credit Risk Estimation. International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 4 (5), (May – 2014), 164-173.
  18. UCI Machine Learning Data Repository – http://archive. ics. uci. edu/ml/datasets.
  19. Tina R. Patil, and S. S. Sherekar. 2013. Performance Analysis of Naive Bayes and J48 Classification Algorithm for Data Classification. International Journal Of Computer Science And Applications Vol. 6, No. 2, (Apr 2013), 256 - 261.
  20. Witten IH, and Frank E. 2005. Data mining: practical machine learning tools and techniques – 2nd ed. the United States of America, Morgan Kaufmann series in data management systems.
  21. Quinlan J (1987) Simplifying decision trees, International Journal of Man Machine Studies, 27(3), 221–234.
  22. S. K. Jayanthi and S. Sasikala. 2013. REPTree Classifier for indentifying Link Spam in Web Search Engines. IJSC, Volume 3, Issue 2, (Jan 2013), 498 – 505.
  23. Leo Breiman. 2001. Random Forests. Machine Learning. 45(1): 5-32.
  24. Margaret H. Danham, and S. Sridhar. 2006. Data mining, Introductory and Advanced Topics. Person education, 1st Edition
Index Terms

Computer Science
Information Sciences

Keywords

Credit Risk Forecast J48 Classifier Proficiency Comparison Random Forest Classifier Rep Tree Classifier.