论文信息 - 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

In this contribution, we employ the recently proposed Kaizen Programming (KP) approach to find 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 real-world credit scoring dataset. When compared to related approaches, KP reaches similar or better results, but evaluates fewer models.

W. Banzhaf | V. V. D. Melo

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