NDLI: Comparison of the primitive classifiers with Extreme Learning Machine in credit scoring

Content Provider	IEEE Xplore Digital Library
Author	Feng-Chia Li Peng-Kai Wang Gwo-En Wang
Copyright Year	2009
Description	Author affiliation: Department of Information Management, Jen-Teh Junior College, Miaoli city, Taiwan, ROC (Feng-Chia Li; Gwo-En Wang) \|\| Department of Information Management, Hwa Hsia College, Taipei city, Taiwan, Country (Peng-Kai Wang)
Abstract	With the rapid growth in the credit industry, credit scoring classifiers are being widely used for credit admission evaluation. Effective classifiers have been regarded as a critical topic, with the related departments striving to collect huge amounts of data to avoid making the wrong decision. Finding effective classifier is important because it will help people make an objective decision instead of them having to rely merely on intuitive experience. This study proposes two well-known classifiers, namely, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), which will be used to find the highest accuracy rate classifier without features selection. Two credit data sets from University of California, Irvine (UCI) are chosen to evaluate the accuracy of various classifiers. The results are compared and the nonparametric Wilcoxon signed rank test will be performed to show if there is any significant difference between these classifiers. Performance of the KNN classifier is better in only one data set but not significant, whereas SVM classifier is significant superior to Extreme Learning Machine (ELM) classifier in the German data set. The result of this study suggests that the primitive classifiers did not achieve satisfactory classification results. Combining with effective feature selection approaches in finding optimal subsets is a promising method in the field of credit scoring.
Starting Page	685
Ending Page	688
File Size	508999
Page Count	4
File Format	PDF
ISBN	9781424448692
DOI	10.1109/IEEM.2009.5373241
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-08
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Extreme Learning Machine Educational institutions Information management Support Vector Machine Data mining Diseases Support vector machines Diversity reception Support vector machine classification Machine learning Cities and towns K Nearest Neighborhood Risk management
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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