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Reseach Article

Loan Application Approval using Meta Classifier

Published on September 2014 by B.rohit, K.p. Supreethi
National Conference on Advances in Technology and Applied Sciences
Foundation of Computer Science USA
NCATAS - Number 1
September 2014
Authors: B.rohit, K.p. Supreethi
a5b696b1-8736-4d57-a69d-d417d80519ca

B.rohit, K.p. Supreethi . Loan Application Approval using Meta Classifier. National Conference on Advances in Technology and Applied Sciences. NCATAS, 1 (September 2014), 34-37.

@article{
author = { B.rohit, K.p. Supreethi },
title = { Loan Application Approval using Meta Classifier },
journal = { National Conference on Advances in Technology and Applied Sciences },
issue_date = { September 2014 },
volume = { NCATAS },
number = { 1 },
month = { September },
year = { 2014 },
issn = 0975-8887,
pages = { 34-37 },
numpages = 4,
url = { /proceedings/ncatas/number1/17947-1609/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Advances in Technology and Applied Sciences
%A B.rohit
%A K.p. Supreethi
%T Loan Application Approval using Meta Classifier
%J National Conference on Advances in Technology and Applied Sciences
%@ 0975-8887
%V NCATAS
%N 1
%P 34-37
%D 2014
%I International Journal of Computer Applications
Abstract

A Meta Classifier in this approach is used for the approval of Loan application, as a Data mining classification tool to support Business operations in a very secure way. The goal of designing a Meta classifier system is to achieve the best possible classification performance for the task of effective decision making. This Meta classifier is the combination of Naïve Bayesian classifier, K-Nearest Neighbor and Fuzzy Set approach. This classifier focuses on combination schemes of multiple classifiers to achieve better classification performance than that obtained by individual models, which in turn is used in providing loans to the customers by verifying the various details relating to the loan such as amount of loan, lending rate, loan term, type of property, income and credit history of the customer etc. The Meta Classifier helps in analyzing the involvement of risk and behavior of the customers by distinguishing borrowers who repay loans promptly from those who don't, hence reducing the loss of revenue.

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Index Terms

Computer Science
Information Sciences

Keywords

Meta Classifier Naïve Bayesian Classifier K- Nearest Neighbor Fuzzy Set Theory