
Rule-Based Out-of-Distribution Detection
Author(s) -
Giacomo De Bernardi,
Sara Narteni,
Enrico Cambiaso,
Maurizio Mongelli
Publication year - 2023
Publication title -
ieee transactions on artificial intelligence
Language(s) - English
Resource type - Journals
ISSN - 2691-4581
DOI - 10.1109/tai.2023.3323923
Subject(s) - computing and processing
Out-of-distribution detection is one of the most critical issue in the deployment of machine learning. The data analyst must assure that data in operation should be compliant with the training phase as well as understand if the environment has changed in a way that autonomous decisions would not be safe anymore. The method of the paper is based on eXplainable Artificial Intelligence (XAI); it takes into account different metrics to identify any resemblance between in-distribution and out of, as seen by the XAI model. The approach is nonparametric and distributional assumption free. The validation over complex scenarios (predictive maintenance, vehicle platooning, covert channels in cybersecurity) corroborates both precision in detection and evaluation of training-operation conditions proximity.