Explainable deep learning: A methodology to train Generalized Additive Models with deep neural networks
Published in International Symposium on Nonparametric Statistics (ISNPS 2024), 2024
Recommended citation: Ortega-Fernandez, I., & Sestelo, M. (2024). Explainable deep learning: A methodology to train Generalized Additive Models with deep neural networks. Poster presentation at ISNPS 2024. https://isnps.unipa.it/
Deep neural networks often suffer from limited interpretability despite their high predictive performance. This work presents a methodology for training Generalized Additive Models using deep neural-network architectures to obtain accurate and explainable machine-learning systems.
The proposed approach independently trains neural networks to estimate feature effects while preserving the additive structure required for transparency and interpretability. The presentation discusses theoretical aspects, optimization strategies, and applications in explainable AI and cybersecurity.