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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.

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Index Terms

Computer Science
Information Sciences

Keywords

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