Automatic composition and optimisation of multicomponent predictive systems.

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

Journal: IEEE Transactions on Neural Networks and Learning Systems

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

ISSN: 2162-2388

Composition and parametrisation of multicomponent predictive systems (MCPSs) consisting of chains of data transformation steps is a challenging task. This paper is concerned with theoretical considerations and extensive experimental analysis for automating the task of building such predictive systems. In the theoretical part of the paper, we first propose to adopt the Well-handled and Acyclic Workflow (WA-WF) Petri net as a formal representation of MCPSs. We then define the optimisation problem in which the search space consists of suitably parametrised directed acyclic graphs (i.e. WA-WFs) forming the sought MCPS solutions.

In the experimental analysis we focus on examining the impact of considerably extending the search space resulting from incorporating multiple sequential data cleaning and preprocessing steps in the process of composing optimised MCPSs, and the quality of the solutions found. In a range of extensive experiments three different optimisation strategies are used to automatically compose MCPSs for 21 publicly available datasets and 7 datasets from real chemical processes. The diversity of the composed MCPSs found is an indication that fully and automatically exploiting different combinations of data cleaning and preprocessing techniques is possible and highly beneficial for different predictive models. Our findings can have a major impact on development of high quality predictive models as well as their maintenance and scalability aspects needed in modern applications and deployment scenarios.

This data was imported from arXiv:

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

http://dx.doi.org/10.1109/TASE.2018.2876430

Journal: in IEEE Transactions on Automation Science and Engineering. (2018) 1-14

DOI: 10.1109/TASE.2018.2876430

Composition and parameterization of multicomponent predictive systems (MCPSs) consisting of chains of data transformation steps are a challenging task.

Auto-WEKA is a tool to automate the combined algorithm selection and hyperparameter (CASH) optimization problem. In this paper, we extend the CASH problem and Auto-WEKA to support the MCPS, including preprocessing steps for both classification and regression tasks. We define the optimization problem in which the search space consists of suitably parameterized Petri nets forming the sought MCPS solutions. In the experimental analysis, we focus on examining the impact of considerably extending the search space (from approximately 22,000 to 812 billion possible combinations of methods and categorical hyperparameters). In a range of extensive experiments, three different optimization strategies are used to automatically compose MCPSs for 21 publicly available data sets. The diversity of the composed MCPSs found is an indication that fully and automatically exploiting different combinations of data cleaning and preprocessing techniques is possible and highly beneficial for different predictive models. We also present the results on seven data sets from real chemical production processes. Our findings can have a major impact on the development of high-quality predictive models as well as their maintenance and scalability aspects needed in modern applications and deployment scenarios.

This data was imported from DBLP:

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

Journal: CoRR

Volume: abs/1612.08789

The data on this page was last updated at 04:53 on March 24, 2019.