Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/148888
Authors: 
Zieba, Maciej
Härdle, Wolfgang
Year of Publication: 
2016
Series/Report no.: 
SFB 649 Discussion Paper 2016-052
Abstract: 
In this work we present a novel ensemble model for a credit scoring problem. The main idea of the approach is to incorporate separate beta binomial distributions for each of the classes to generate balanced datasets that are further used to construct base learners that constitute the final ensemble model. The sampling procedure is performed on two separate ranking lists, each for one class, where the ranking is based on prepotency of observing positive class. Two strategies are considered: one assumes mining easy examples and the second one forces good classification of hard cases. The proposed solutions are tested on two big datasets on credit scoring.
Subjects: 
credit scoring
ensemble model
beta distribution
Beta boost
big data
JEL: 
C53
Document Type: 
Working Paper

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