Explainable predictive maintenance: a survey of current methods, challenges and opportunities
Predictive maintenance is a well studied collection of techniques that aims to prolong the life
of a mechanical system by using artificial intelligence and machine learning to predict the …
of a mechanical system by using artificial intelligence and machine learning to predict the …
[HTML][HTML] Information flow-based fuzzy cognitive maps with enhanced interpretability
M Tyrovolas, XS Liang, C Stylios - Granular Computing, 2023 - Springer
Abstract Fuzzy Cognitive Maps (FCMs) are a graph-based methodology successfully
applied for knowledge representation of complex systems modelled through an interactive …
applied for knowledge representation of complex systems modelled through an interactive …
BELLATREX: Building explanations through a locally accurate rule extractor
Random forests are machine learning methods characterised by high performance and
robustness to overfitting. However, since multiple learners are combined, they are not as …
robustness to overfitting. However, since multiple learners are combined, they are not as …
Local Interpretability of Random Forests for Multi-Target Regression
Multi-target regression is useful in a plethora of applications. Although random forest models
perform well in these tasks, they are often difficult to interpret. Interpretability is crucial in …
perform well in these tasks, they are often difficult to interpret. Interpretability is crucial in …
[PDF][PDF] Exploring rule-based interpretability of random forests in multi-target regression
A Bardos - 2023 - ikee.lib.auth.gr
The constant content generation of today's society has led companies to include an
increasing amount of complex data in their decision support systems. Given situations where …
increasing amount of complex data in their decision support systems. Given situations where …
[PDF][PDF] Local interpretability of random forests for multi-target regression
I Mollas, G Tsoumakas - project.inria.fr
Multi-target regression is useful in a plethora of applications. Although random forest models
perform well in these tasks, they are often difficult to interpret. Interpretability is crucial in …
perform well in these tasks, they are often difficult to interpret. Interpretability is crucial in …