João Kenji Suwa, João Pedro Silveira e Silva, Bruno Iochins Grisci · 2026 · arXiv
DOI: 10.48550/arXiv.2610.31420 · open-access PDF
The interpretability of complex neural networks remains a critical challenge, especially for models already deployed in high-stakes domains. To address this, we adapted the Feature Selection Layer (FSL) to optimize feature weights post-training, a variant we term post-hoc FSL. Our approach reframes the FSL as a lightweight, trainable module that integrates with already frozen pre-trained models on tabular datasets to highlight the features the original model considers most important. This post-hoc FSL learns feature relevance by fine-tuning its weights based on the original model's learned out
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