Towards a more reliable interpretation of machine learning outputs for safety-critical systems using feature importance fusion
(2021)
Journal Article
Rengasamy, D., Rothwell, B. C., & Figueredo, G. P. (2021). Towards a more reliable interpretation of machine learning outputs for safety-critical systems using feature importance fusion. Applied Sciences, 11(24), Article 11854. https://doi.org/10.3390/app112411854
When machine learning supports decision-making in safety-critical systems, it is important to verify and understand the reasons why a particular output is produced. Although feature importance calculation approaches assist in interpretation, there is... Read More about Towards a more reliable interpretation of machine learning outputs for safety-critical systems using feature importance fusion.