Abstract
Credit scoring refers to the use of statistical models to support loan approval decisions. An ever-increasing availability of data on potential borrowers emphasizes the importance of feature selection for scoring models. Traditionally, feature selection has been viewed as a single-objective task. Recent research demonstrates the effectiveness of multi-objective approaches. We propose a novel multi-objective feature selection framework for credit scoring that extends previous work by taking into account data acquisition costs and employing a state-of-the-art particle swarm optimization algorithm. Our framework optimizes three fitness functions: the number of features, data acquisition costs and the AUC. Experiments on nine credit scoring data sets demonstrate a highly competitive performance of the proposed framework.
Cite
@inproceedings{kozodoi2020multiobjective,
title={Multi-Objective Particle Swarm Optimization for Feature Selection in Credit Scoring},
author={Kozodoi, N. and Lessmann, S.},
booktitle={{ECML} {PKDD} 2020 Workshop on Mining Data for Financial Applications},
year={2020}
}
