A Data Driven Ensemble Classifier for Credit Scoring Analysis View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2009

AUTHORS

Nan-Chen Hsieh , Lun-Ping Hung , Chia-Ling Ho

ABSTRACT

This study focuses on predicting whether a credit applicant can be categorized as good, bad or borderline from information initially supplied. Given its importance, many researchers have recently worked on an ensemble of classifiers. However, to the best of our knowledge, unrepresentative samples drastically reduce the accuracy of the deployment classifier. Few have attempted to preprocess the input samples into more homogeneous cluster groups and then fit the ensemble classifier accordingly. For this reason, we introduce the concept of class-wise classification as a preprocessing step in order to obtain an efficient ensemble classifier. This strategy would work better than a direct ensemble of classifiers without the preprocessing step. The proposed ensemble classifier is constructed by incorporating several data mining techniques, mainly involving optimal associate binning to discretize continuous values; neural network, support vector machine, and Bayesian network are used to augment the ensemble classifier. In particular, the Markov blanket concept of Bayesian network allows for a natural form of feature selection, which provides a basis for mining association rules. More... »

PAGES

351-362

Book

TITLE

Advances in Knowledge Discovery and Data Mining

ISBN

978-3-642-01306-5
978-3-642-01307-2

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-01307-2_33

DOI

http://dx.doi.org/10.1007/978-3-642-01307-2_33

DIMENSIONS

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