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Erschienen in:
Statistics, Statisticians, and the Internet of Things Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

21. Beta-Boosted Ensemble for Big Credit Scoring Data

verfasst von : Maciej Zieba, Wolfgang Karl Härdle

Erschienen in: Handbook of Big Data Analytics

Verlag: Springer International Publishing

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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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Vorheriges Kapitel Construction of Tight Frames on Graphs and Application to Denoising
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Metadaten
Titel
Beta-Boosted Ensemble for Big Credit Scoring Data
verfasst von
Maciej Zieba
Wolfgang Karl Härdle
Copyright-Jahr
2018
Buch
Handbook of Big Data Analytics

Print ISBN: 978-3-319-18283-4

Electronic ISBN: 978-3-319-18284-1

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-18284-1_21

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