Contextual Explanations for Decision Support in Predictive Maintenance
Explainable artificial intelligence (XAI) methods aim to explain to the user on what basis the model makes decisions. Unfortunately, general-purpose approaches that are independent of the types of data, model used and the level of sophistication of the user are not always able to make model decision...
Main Author: | Michał Kozielski |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-09-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/13/18/10068 |
Similar Items
-
Sensor-Based Predictive Maintenance with Reduction of False Alarms—A Case Study in Heavy Industry
by: Marek Hermansa, et al.
Published: (2021-12-01) -
RuleXAI—A package for rule-based explanations of machine learning model
by: Dawid Macha, et al.
Published: (2022-12-01) -
Reference-Based AI Decision Support for Cybersecurity
by: Hyun-Woo Lee, et al.
Published: (2023-01-01) -
Deterministic Local Interpretable Model-Agnostic Explanations for Stable Explainability
by: Muhammad Rehman Zafar, et al.
Published: (2021-06-01) -
Explainable AI Evaluation: A Top-Down Approach for Selecting Optimal Explanations for Black Box Models
by: SeyedehRoksana Mirzaei, et al.
Published: (2023-12-01)