Abstract
In this work we present the novel ensemble model for 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 probability of observing positive class. The two strategies are considered in the studies: one assumes mining easy examples and the second one force good classification of hard cases. The proposed solutions are tested on two big datasets from credit scoring domain.
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Zieba, M., Härdle, W.K. (2018). Beta-Boosted Ensemble for Big Credit Scoring Data. In: Härdle, W., Lu, HS., Shen, X. (eds) Handbook of Big Data Analytics. Springer Handbooks of Computational Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-18284-1_21
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DOI: https://doi.org/10.1007/978-3-319-18284-1_21
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