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A feature selection enabled hybrid-bagging algorithm for credit risk evaluation

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Title A feature selection enabled hybrid-bagging algorithm for credit risk evaluation
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Creator Shashi Dahiya
S.S Handa
N.P Singh
 
Subject Bagging
Credit risk
Ensemble
Feature selection
Hybrid methods
 
Description Not Available
Hybrid models based on feature selection and machine learning techniques have significantly enhanced the accuracy of standalone models. This paper presents a feature selection‐based hybrid‐bagging algorithm (FS‐HB) for improved credit risk evaluation. The 2 feature selection methods chi‐square and principal component analysis were used for ranking and selecting the important features from the datasets. The classifiers were built on 5 training and test data partitions of the input data set. The performance of the hybrid algorithm was compared with that of the standalone classifiers: feature selection‐based classifiers and bagging. The hybrid FS‐HB algorithm performed best for qualitative dataset with less features and tree‐based unstable base
classifier. Its performance on numeric data was also better than other standalone classifiers, whereas comparable to bagging with only selected features. Its performance was found better on 70:30 data partition and the type II error, which is very significant in risk evaluation was also reduced significantly. The improved performance of FS‐HB is attributed to the important features used for developing the classifier thereby reducing the complexity of the algorithm and the use of ensemble methodology, which added to the classical bias variance trade‐off and performed better than standalone classifiers.
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Date 2018-02-21T11:51:51Z
2018-02-21T11:51:51Z
2017-12-04
 
Type Research Paper
 
Identifier Not Available
Not Available
http://krishi.icar.gov.in/jspui/handle/123456789/5838
 
Language English
 
Relation Not Available;
 
Publisher Wiley