Adapting Multicomponent Predictive Systems using Hybrid Adaptation Strategies with Auto-WEKA in Process Industry

Authors: Salvador, Budka, M. and Gabrys, B.

Conference: AutoML 2016 @ ICML

Dates: 20-24 June 2016

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.

http://eprints.bournemouth.ac.uk/24110/

Source: Manual

Adapting Multicomponent Predictive Systems using Hybrid Adaptation Strategies with Auto-WEKA in Process Industry.

Authors: Salvador, M.M., Budka, M. and Gabrys, B.

Editors: Hutter, F., Kotthoff, L. and Vanschoren, J.

Journal: AutoML@ICML

Volume: 64

Pages: 48-57

Publisher: JMLR.org

http://eprints.bournemouth.ac.uk/24110/

http://proceedings.mlr.press/v64/

Source: DBLP

Adapting Multicomponent Predictive Systems using Hybrid Adaptation Strategies with Auto-WEKA in Process Industry

Authors: Salvador, M.M., Budka, M. and Gabrys, B.

Conference: AutoML 2016 @ ICML

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.

http://eprints.bournemouth.ac.uk/24110/

Source: BURO EPrints