Explainability and Transparency of Classifiers for Air-Handling Unit Faults Using Explainable Artificial Intelligence (XAI)

In recent years, explainable artificial intelligence (XAI) techniques have been developed to improve the explainability, trust and transparency of machine learning models. This work presents a method that explains the outputs of an air-handling unit (AHU) faults classifier using a modified XAI techn...

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Detalles Bibliográficos
Autores principales: Meas, Molika, Machlev, Ram, Kose, Ahmet, Tepljakov, Aleksei, Loo, Lauri, Levron, Yoash, Petlenkov, Eduard, Belikov, Juri
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9460735/
https://www.ncbi.nlm.nih.gov/pubmed/36080795
http://dx.doi.org/10.3390/s22176338

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9460735/
https://www.ncbi.nlm.nih.gov/pubmed/36080795
http://dx.doi.org/10.3390/s22176338

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