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A Neuro-Symbolic Explainer for Rare Events: A Case Study on Predictive Maintenance
April 24, 2024, 4:41 a.m. | Jo\~ao Gama, Rita P. Ribeiro, Saulo Mastelini, Narjes Davarid, Bruno Veloso
cs.LG updates on arXiv.org arxiv.org
Abstract: Predictive Maintenance applications are increasingly complex, with interactions between many components. Black box models are popular approaches based on deep learning techniques due to their predictive accuracy. This paper proposes a neural-symbolic architecture that uses an online rule-learning algorithm to explain when the black box model predicts failures. The proposed system solves two problems in parallel: anomaly detection and explanation of the anomaly. For the first problem, we use an unsupervised state of the art …
abstract accuracy algorithm applications architecture arxiv black box box case case study components cs.ai cs.lg deep learning deep learning techniques events explainer interactions maintenance neuro paper popular predictive predictive maintenance study type
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