← Back to all news
arXiv Machine Learning By Jo\~ao Pedro Silveira e Silva, M\'arcio Dorn, Bruno Iochins Grisci

A Feature Selection Layer Approach for Multi-Class Classification on Neural Networks

Read the original on arXiv Machine Learning →

The paper introduces extensions to the Feature Selection Layer, an embedded feature weighting mechanism for neural networks, to improve interpretability in multi‑class classification. Experiments show that the best extension matches existing methods in identifying globally relevant features while outperforming them in capturing label‑specific relevance. The approach leverages the neural network’s internal dynamics, eliminating the need for post‑processing to reveal how individual features influence specific label predictions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Oct 23

Post-Hoc Feature Selection Layer for Neural Networks Interpretability

The paper introduces a post‑hoc Feature Selection Layer (FSL) that can be added to frozen pre‑trained neural networks on tabular data to highlight the most important features. This lightweight, trainable module fine‑tunes feature weights based on the original model’s outputs without altering its architecture or parameters. Experiments on synthetic and real‑world datasets show that the post‑hoc FSL preserves predictive performance while improving interpretability, outperforming other post‑hoc methods such as Integrated Gradients and SHAP.

By Jo\~ao Kenji Suwa, Jo\~ao Pedro Silveira e Silva, Bruno Iochins Grisci
fine-tuningbenchmarkssafety
More like this →