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Using Optimized Optimization Criteria in Ensemble Member Selection
University of Borås, School of Business and IT.
University of Borås, School of Business and IT.
2009 (English)Conference 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. This paper presents a novel technique, where genetic algorithms are used for combining several measurements into a complex criterion that is optimized separately for each dataset. 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 other measures alone, e.g., estimated ensemble accuracy or the diversity measure difficulty.

Place, publisher, year, edition, pages
2009.
Keyword [en]
ensembles, diversity, Computer Science, Machine Learning, Data Mining
Keyword [sv]
data mining
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:hb:diva-6054Local ID: 2320/4410OAI: oai:DiVA.org:hb-6054DiVA: diva2:886738
Conference
SWIFT 2008 - Skövde Workshop on Information Fusion Topics
Note

Sponsorship:

This work was supported by the Information Fusion Research Program (www.infofusion.se) at the University of Skövde, Sweden, in partnership with the Swedish Knowledge Foundation under grant 2003/0104.

Available from: 2015-12-22 Created: 2015-12-22 Last updated: 2017-05-02

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Löfström, TuveJohansson, Ulf
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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