A key aspect of automating predictive machine learning entails the capability of properly triggering the update of the trained model. To this aim, suitable automatic solutions to self-assess the prediction quality and the data distribution drift between the original training set and the new data have to be devised. In this paper, we propose a novel methodology to automatically detect prediction-quality degradation of machine learning models due to class-based concept drift, i.e., when new data contains samples that do not fit the set of class labels known by the currently-trained predictive model. Experiments on synthetic and real-world public datasets show the effectiveness of the proposed methodology in automatically detecting and describing concept drift caused by changes in the class-label data distributions.
@article{arxiv.1907.08120,
title = {Automating concept-drift detection by self-evaluating predictive model degradation},
author = {Tania Cerquitelli and Stefano Proto and Francesco Ventura and Daniele Apiletti and Elena Baralis},
journal= {arXiv preprint arXiv:1907.08120},
year = {2019}
}