Adapting Multicomponent Predictive Systems Using Hybrid Adaptation Strategies with Auto-WEKA in Process Industry
Abstract
Automation of composition and optimisation of multicomponent predictive systems (MCPSs) made of a number of preprocessing steps and predictive models is a challenging problem that has been addressed in recent works. However, one of the current challenges is how to adapt these systems in dynamic environments where data is changing over time. In this work we propose a hybrid approach combining different adaptation strategies with the Bayesian optimisation techniques for parametric, structural and hyperparameter optimisation of entire MCPSs. Experiments comparing different adaptation strategies have been performed on 7 datasets from real chemical production processes. Experimental analysis shows that optimisation of entire MCPSs as a method of adaptation to changing environments is feasible and that hybrid strategies perform better in most of the analysed cases.
Cite
Text
Salvador et al. "Adapting Multicomponent Predictive Systems Using Hybrid Adaptation Strategies with Auto-WEKA in Process Industry." Proceedings of the Workshop on Automatic Machine Learning, 2016.Markdown
[Salvador et al. "Adapting Multicomponent Predictive Systems Using Hybrid Adaptation Strategies with Auto-WEKA in Process Industry." Proceedings of the Workshop on Automatic Machine Learning, 2016.](https://mlanthology.org/automl/2016/salvador2016automl-adapting/)BibTeX
@inproceedings{salvador2016automl-adapting,
title = {{Adapting Multicomponent Predictive Systems Using Hybrid Adaptation Strategies with Auto-WEKA in Process Industry}},
author = {Salvador, Manuel Martin and Budka, Marcin and Gabrys, Bogdan},
booktitle = {Proceedings of the Workshop on Automatic Machine Learning},
year = {2016},
pages = {48-57},
volume = {64},
url = {https://mlanthology.org/automl/2016/salvador2016automl-adapting/}
}