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Super computer heterogeneous classifier meta-ensembles

Studley, Matthew; Bagnall, A J; Bull, L; Whittley, I M

Authors

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Dr Matthew Studley Matthew2.Studley@uwe.ac.uk
Professor of Ethics & Technology/School Director (Research & Enterprise)

A J Bagnall

L Bull

I M Whittley



Journal Article Type Article
Publication Date Apr 1, 2007
Journal International Journal of Data Warehousing and Mining
Print ISSN 1548-3924
Publisher IGI Global
Peer Reviewed Not Peer Reviewed
Volume 3
Issue 2
Pages 67-82
DOI https://doi.org/10.4018/IJDWM
Keywords super computer, heterogeneous classifier, meta-ensembles
Public URL https://uwe-repository.worktribe.com/output/1028588
Publisher URL http://dx.doi.org/10.4018/IJDWM
Additional Information Additional Information : As the use of machine learning for exploratory data analysis has increased, so have the sizes of the datasets they must face and the sophistication of the algorithms themselves. For this reason there is a growing body of research concerned with the use of parallel computing for data mining. The Supercomputer Data Mining (SCDM) Project (EPSRC project (GR/T18455/01)) produced a super-computing data mining resource for use by the UK academic community. In particular, a number of evolutionary computing-based algorithms and the ensemble machine approach were used to exploit the large-scale parallelism possible in super-computing. In an ensemble machine many classifiers can be used to increase accuracy and speed of classification through parallelism. This paper presents results of using ensembles of heterogeneous classifier techniques, which may themselves be ensembles of classifiers. The increased accuracy thus achieved would only be practical given a Supercomputer resource. The techniques and implementation detailed herein are now in use in data mining UK Olympic training data, England cricket bowling data, cancer data, etc.



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