This study evaluates the impact of predicting using neural networks that have not been retrained after feature selection.
We show that non-retrained neural networks remain competitive, achieving a degradation rate of less than 10% even when features are removed. 4. We demonstrate ...
Vol 28, No 3 (2024): 28(3) 2024 - Articles Evaluating the Impact of Removing Low-relevance Features in Non-retrained Neural Networks Abstract PDF. ISSN ...
Vol 28, No 3 (2024): 28(3) 2024 - Articles Evaluating the Impact of Removing Low-relevance Features in Non-retrained Neural Networks Abstract PDF. ISSN ...
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We present relevance aggregation, an algorithm that combines the relevance computed from several samples as learned by a neural network and generates scores ...
Mar 5, 2024 · Machine unlearning refers to the process of selectively removing specific training data points and their influence on an already trained model.
Sep 4, 2024 · Evaluating the Impact of Removing Low-relevance Features in Non-retrained Neural Networks. Article. Sep 2024. Uriel Corona-Bermúdez ...
Mar 9, 2020 · We introduce IROF (Iterative Removal Of Features) as a new approach to quantitatively evaluate explanation methods without relying on human ...
Nov 28, 2023 · Learn how to identify, remove, and evaluate the impact of noisy and irrelevant features on your ML model using different techniques and ...
This implies that a strong performance degradation without re-training might be caused by a shift in distribution instead of removal of information. Instead, in ...
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