On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper)
2024/01/01 by Gauriat, Charles-Maxime, Pencolé, Yannick, Ribot, Pauline +5 · 75 citations
Computer Science · Engineering · #Bayesian Modeling and Causal Inference #Engineering Diagnostics and Reliability #Explainable Artificial Intelligence (XAI) #Machine Learning and Data Classification
paper · pdf · doi:10.4230/oasics.dx.2024.27
openalex publication_date 2024/01/01 · openalex created_date 2025/01/14 · openalex updated_date 2026/07/29
Abstract
In an industrial maintenance context, degradation diagnosis is the problem of determining the current level of degradation of operating machines based on measurements. With the emergence of Machine Learning techniques, such a problem can now be solved by training a degradation model offline and by using it online. While such models are more and more accurate and performant, they are often black-box and their decisions are therefore not interpretable for human maintenance operators. On the contrary, interpretable ML models are able to provide explanations for the model’s decisions and consequently improves the confidence of the human operator about the maintenance decision based on these models. This paper proposes a new method to quantitatively measure the interpretability of such models that is agnostic (no assumption about the class of models) and that is applied on degradation models. The proposed method requires that the decision maker sets up some high level parameters in order to measure the interpretability of the models and then can decide whether the obtained models are satisfactory or not. The method is formally defined and is fully illustrated on a decision tree degradation model and a model trained with a recent neural network architecture called Multiclass Neural Additive Model.
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- Definitions, methods, and applications in interpretable machine learning
- Explainable Image Similarity: Integrating Siamese Networks and Grad-CAM
- The Bouncer Problem: Challenges to Remote Explainability
- All-printed soft human-machine interface for robotic physicochemical sensing
- Neural Forecasting of the Italian Sovereign Bond Market with Economic News
- Wider Vision: Enriching Convolutional Neural Networks via Alignment to External Knowledge Bases
- A Multistakeholder Approach Towards Evaluating AI Transparency Mechanisms
- Sanity Simulations for Saliency Methods
- Marginal Contribution Feature Importance -- an Axiomatic Approach for The Natural Case
- Machine learning in critical heat flux studies in nuclear systems: A detailed review
- Enhancing interpretability in neural networks for nuclear power plant fault diagnosis: A comprehensive analysis and improvement approach
- Learning Variational Word Masks to Improve the Interpretability of Neural Text Classifiers
- Explainable Machine Learning for Predicting Homicide Clearance in the United States
- Boosting Algorithms for Estimating Optimal Individualized Treatment Rules
- iSEA: An Interactive Pipeline for Semantic Error Analysis of NLP Models
- From local explanations to global understanding with explainable AI for trees
- Augmenting the availability of historical GDP per capita estimates through machine learning
- Interpreted machine learning in fluid dynamics: explaining relaminarisation events in wall-bounded shear flows
- Shedding Light on Black Box Machine Learning Algorithms: Development of an Axiomatic Framework to Assess the Quality of Methods that Explain Individual Predictions
- Infusing domain knowledge in AI-based "black box" models for better explainability with application in bankruptcy prediction
- Binary Stochastic Filtering: feature selection and beyond
- XEM: An explainable-by-design ensemble method for multivariate time series classification
- Multi-objective optimization and explanation for stroke risk assessment in Shanxi province
- Learning Policies from Self-Play with Policy Gradients and MCTS Value\n Estimates
- Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities
- Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring
- Interpretable Deep Learning for Automatic Diagnosis of 12-lead Electrocardiogram
- Cohort Shapley value for algorithmic fairness
- Exploring Layerwise Decision Making in DNNs
- Explaining Anomalies Detected by Autoencoders Using SHAP
- Explaining Explanations in AI
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