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Ensemble member selection using multi-objective optimization
University of Borås, Sweden.ORCID iD: 0000-0003-0274-9026
University of Borås, Sweden.
University of Skövde, Sweden.
2009 (English)In: IEEE Symposium on Computational Intelligence and Data Mining, 2009, p. 245-251Conference paper, Published 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
2009. p. 245-251
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:kth:diva-221588DOI: 10.1109/CIDM.2009.4938656ISI: 000271487700035Scopus ID: 2-s2.0-67650434708ISBN: 978-1-4244-2765-9 (print)OAI: oai:DiVA.org:kth-221588DiVA, id: diva2:1175212
Conference
IEEE Symposium on Computational Intelligence and Data Mining (CIDM), Nashville, TN, March 30 2009-April 2 2009
Note

QC 20180209

Available from: 2018-01-17 Created: 2018-01-17 Last updated: 2018-02-09Bibliographically approved

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Löfström, TuveBoström, Henrik
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CiteExportLink to record
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Citation style
  • apa
  • ieee
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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