Profit-Oriented Feature Selection in Credit Scoring Applications

Nikita Kozodoi (), Stefan Lessmann, Bart Baesens and Konstantinos Papakonstantinou
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Nikita Kozodoi: Humboldt University of Berlin
Stefan Lessmann: Humboldt University of Berlin
Bart Baesens: Catholic University of Leuven
Konstantinos Papakonstantinou: Kreditech

A chapter in Operations Research Proceedings 2018, 2019, pp 59-65 from Springer

Abstract: Abstract In credit scoring, feature selection aims at removing irrelevant data to improve the performance of the scorecard and its interpretability. Standard feature selection techniques are based on statistical criteria such as correlation. Recent studies suggest that using profit-based indicators for model evaluation may improve the quality of scoring models for businesses. We extend the use of profit measures to feature selection and develop a wrapper-based framework that uses the Expected Maximum Profit measure (EMP) as a fitness function. Experiments on multiple credit scoring data sets provide evidence that EMP-maximizing feature selection helps to develop scorecards that yield a higher expected profit compared to conventional feature selection strategies.

Keywords: Feature selection; Credit scoring; Profit maximization (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:spr:oprchp:978-3-030-18500-8_9

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DOI: 10.1007/978-3-030-18500-8_9

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