Towards automatic composition of multicomponent predictive systems

Martin Salvador, M; Budka, M; Gabrys, B

Towards automatic composition of multicomponent predictive systems

Martin Salvador, M Budka, M Gabrys, B

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Publisher:: Springer
Publication Type:: Conference Proceeding
Citation:: Hybrid Artificial Intelligent Systems, 2016, 9648, pp. 27-39
Issue Date:: 2016-01-01

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Field	Value	Language
dc.contributor.author	Martin Salvador, M
dc.contributor.author	Budka, M
dc.contributor.author	Gabrys, B https://orcid.org/0000-0002-0790-2846
dc.contributor.editor	MartinezAlvarez, F
dc.contributor.editor	Troncoso, A
dc.contributor.editor	Quintian, H
dc.contributor.editor	Corchado, E
dc.date	2016-04-18
dc.date.accessioned	2023-02-23T20:15:43Z
dc.date.available	2023-02-23T20:15:43Z
dc.date.issued	2016-01-01
dc.identifier.citation	Hybrid Artificial Intelligent Systems, 2016, 9648, pp. 27-39
dc.identifier.isbn	9783319320335
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/166403
dc.description.abstract	Automatic composition and parametrisation of multicomponent predictive systems (MCPSs) consisting of chains of data transformation steps is a challenging task. In this paper we propose and describe an extension to the Auto-WEKA software which now allows to compose and optimise such flexible MCPSs by using a sequence of WEKA methods. In the experimental analysis we focus on examining the impact of significantly extending the search space by incorporating additional hyperparameters of the models, on the quality of the found solutions. In a range of extensive experiments three different optimisation strategies are used to automatically compose MCPSs on 21 publicly available datasets. A comparison with previous work indicates that extending the search space improves the classification accuracy in the majority of the cases. The diversity of the found MCPSs are also an indication that fully and automatically exploiting different combinations of data cleaning and preprocessing techniques is possible and highly beneficial for different predictive models. This can have a big impact on high quality predictive models development, maintenance and scalability aspects needed in modern application and deployment scenarios.
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Hybrid Artificial Intelligent Systems
dc.relation.ispartof	International Conference on Hybrid Artificial Intelligence Systems
dc.relation.ispartofseries	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.isbasedon	10.1007/978-3-319-32034-2_3
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Towards automatic composition of multicomponent predictive systems
dc.type	Conference Proceeding
utslib.citation.volume	9648
utslib.location.activity	Seville, Spain
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Centre for Health Technologies (CHT)
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-02-23T20:15:41Z
pubs.finish-date	2016-04-20
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
pubs.start-date	2016-04-18
pubs.volume	9648
dc.location	Switzerland

Abstract:

Automatic composition and parametrisation of multicomponent predictive systems (MCPSs) consisting of chains of data transformation steps is a challenging task. In this paper we propose and describe an extension to the Auto-WEKA software which now allows to compose and optimise such flexible MCPSs by using a sequence of WEKA methods. In the experimental analysis we focus on examining the impact of significantly extending the search space by incorporating additional hyperparameters of the models, on the quality of the found solutions. In a range of extensive experiments three different optimisation strategies are used to automatically compose MCPSs on 21 publicly available datasets. A comparison with previous work indicates that extending the search space improves the classification accuracy in the majority of the cases. The diversity of the found MCPSs are also an indication that fully and automatically exploiting different combinations of data cleaning and preprocessing techniques is possible and highly beneficial for different predictive models. This can have a big impact on high quality predictive models development, maintenance and scalability aspects needed in modern application and deployment scenarios.

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http://hdl.handle.net/10453/166403