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Ensemble Member Selection Using Multi-Objective Optimization
School of Business and Informatics, University o f Borås, Borås, Sweden.
School of Business and Informatics, University o f Borås, Borås, Sweden.
University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
2009 (English)In: Proceedings of IEEE Symposium on Computational Intelligence and Data Mining (CIDM), IEEE conference proceedings, 2009, 245-251 p.Conference paper, (Refereed)
Abstract [en]

Both theory and a wealth of empirical studies have established that ensembles are more accurate than single predictive models. Unfortunately, the problem of how to maximize ensemble accuracy is, especially for classification, far from solved. In essence, the key problem is to find a suitable criterion, typically based on training or selection set performance, highly correlated with ensemble accuracy on novel data. Several studies have, however, shown that it is difficult to come up with a single measure, such as ensemble or base classifier selection set accuracy, or some measure based on diversity, that is a good general predictor for ensemble test accuracy. This paper presents a novel technique that for each learning task searches for the most effective combination of given atomic measures, by means of a genetic algorithm. Ensembles built from either neural networks or random forests were empirically evaluated on 30 UCI datasets. The experimental results show that when using the generated combined optimization criteria to rank candidate ensembles, a higher test set accuracy for the top ranked ensemble was achieved, compared to using ensemble accuracy on selection data alone. Furthermore, when creating ensembles from a pool of neural networks, the use of the generated combined criteria was shown to generally outperform the use of estimated ensemble accuracy as the single optimization criterion.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2009. 245-251 p.
National Category
Computer and Information Science
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-3212DOI: 10.1109/CIDM.2009.4938656ISI: 000271487700035Scopus ID: 2-s2.0-67650434708ISBN: 978-1-4244-2765-9 OAI: oai:DiVA.org:his-3212DiVA: diva2:225390
Conference
2009 IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2009) proceedings : March 30-April 2, 2009, Sheraton Music City Hotel, Nashville, TN, USA
Available from: 2009-06-26 Created: 2009-06-26 Last updated: 2015-12-28Bibliographically approved

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Löfström, TuveBoström, Henrik
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf