Improving logistic regression classification of credit approval with features constructed by kaizen programming

Improving logistic regression classification of credit approval with features constructed by kaizen programming

Author de Melo, Vinicius Veloso Autor UNIFESP Google Scholar
Banzhaf, Wolfgang Google Scholar
Abstract In this contribution, we employ the recently proposed Kaizen Programming (KP) approach to fi nd high-quality nonlinear combinations of the original features in a dataset. KP constructs many complementary features at the same time, which are selected by their importance, not by model quality. We investigated our approach in a well-known realworld credit scoring dataset. When compared to related approaches, KP reaches similar or better results, but evaluates fewer models.
Keywords Credit Approval
Logistic Regression
Classification
Kaizen Programming
Language English
Date 2016
Published in Proceedings Of The 2016 Genetic And Evolutionary Computation Conference (GECCO'16 Companion). New york, p. 61-62, 2016.
Publisher Funpec-Editora
Extent 61-62
Origin http://dx.doi.org/10.1145/2908961.2908963
Access rights Closed access
Type Conference paper
Web of Science ID WOS:000383741800031
URI http://repositorio.unifesp.br/handle/11600/49401

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