NDLI: Analysis of feature selection techniques in credit risk assessment

Content Provider	IEEE Xplore Digital Library
Author	Ramya, R.S. Kumaresan, S.
Copyright Year	2015
Description	Author affiliation: Dept. of CSE, Gov. Coll. of Technol., Coimbatore, India (Ramya, R.S.; Kumaresan, S.)
Abstract	Data Mining is an automated extraction of hidden knowledge from large amount of data. The computational complexity of the data mining algorithms increases rapidly as the number of features in the dataset increases. Real world credit datasets have accumulated large quantities of information about clients and their financial and payment history. Feature selection techniques are used on such high dimensional data to reduce the dimensionality by removing irrelevant and redundant features to improve the predictive accuracy of data mining algorithms. The objective of this work is study the information gain, gain ratio and chi square correlation based feature selection method to reduce the feature dimensionality. Information gain measure identifies the entropy value of each specific feature. The amount of information gain or entropy is used to decide whether the feature is selected or deleted. Gain ratio applies normalization technique to information gain using spilt information value. The correlation based feature selection uses heuristic search strategies to estimate how the features are correlated with the class attribute and how they are important of each other. Experiments were conducted on the German credit dataset available at UCI Machine Learning Repository to reduce the feature dimensionality using these feature selection methods.
Starting Page	1
Ending Page	6
File Size	239289
Page Count	6
File Format	PDF
e-ISBN	9781479964383
DOI	10.1109/ICACCS.2015.7324139
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-05
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Correlation Feature selection Communication systems Information gain Data Mining Entropy Data mining History Credit risk assessment Gain ratio Filtering algorithms Prediction algorithms Chi square correlation
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
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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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