March 28, 2024, 4:42 a.m. | Dennis Gross, Helge Spieker, Arnaud Gotlieb, Ricardo Knoblauch

cs.LG updates on arXiv.org arxiv.org

arXiv:2403.18731v1 Announce Type: cross
Abstract: This research presents a method that utilizes explainability techniques to amplify the performance of machine learning (ML) models in forecasting the quality of milling processes, as demonstrated in this paper through a manufacturing use case. The methodology entails the initial training of ML models, followed by a fine-tuning phase where irrelevant features identified through explainability methods are eliminated. This procedural refinement results in performance enhancements, paving the way for potential reductions in manufacturing costs and …

abstract amplify arxiv case cs.ai cs.cv cs.cy cs.lg explainability forecasting integration machine machine learning manufacturing methodology ml models paper performance prediction prediction models processes quality research through training type

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