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Stacking-based heterogeneous genetic programming for interpretable credit risk evaluation
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DOI：10.1016/j.asoc.2025.114214.png)
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Abstract
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• An end-to-end pipeline for generating a heterogeneous ensemble model is proposed.. • Genetic programming is used as the meta-classifier in a stacking credit risk model. • NGBoost and TabNet are used as base-classifier. • Our SH-GPC model outperforms meta-classifiers and provides better interpretability. • Visualization methods are used to enhance the trustworthiness of the stacking model.
Keywords:
Credit risk prediction
Heterogeneous stacking
Genetic programming
Shapley additive explanations
Natural gradient boosting
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