Salvador, M. M., Budka, M. and Gabrys, B., 2016. Adapting Multicomponent Predictive Systems using Hybrid Adaptation Strategies with Auto-WEKA in Process Industry. In: AutoML 2016 @ ICML, 20-24 June 2016, New York (USA).
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MartinSalvador-paper.pdf - Accepted Version
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.
|Item Type:||Conference or Workshop Item (Paper)|
|Group:||Faculty of Science & Technology|
|Deposited By:||Unnamed user with email symplectic@symplectic|
|Deposited On:||22 Jun 2016 08:31|
|Last Modified:||29 Jun 2016 14:51|
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