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Utilizing Diversity and Performance Measures for Ensemble Creation
University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
2009 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

An ensemble is a composite model, aggregating multiple base models into one predictive model. An ensemble prediction, consequently, is a function of all included base models. Both theory and a wealth of empirical studies have established that ensembles are generally more accurate than single predictive models. The main motivation for using ensembles is the fact that combining several models will eliminate uncorrelated base classifier errors. This reasoning, however, requires the base classifiers to commit their errors on different instances – clearly there is no point in combining identical models. Informally, the key term diversity means that the base classifiers commit their errors independently of each other. The problem addressed in this thesis is how to maximize ensemble performance by analyzing how diversity can be utilized when creating ensembles. A series of studies, addressing different facets of the question, is presented. The results show that ensemble accuracy and the diversity measure difficulty are the two individually best measures to use as optimization criterion when selecting ensemble members. However, the results further suggest that combinations of several measures are most often better as optimization criteria than single measures. A novel method to find a useful combination of measures is proposed in the end. Furthermore, the results show that it is very difficult to estimate predictive performance on unseen data based on results achieved with available data. Finally, it is also shown that implicit diversity achieved by varied ANN architecture or by using resampling of features is beneficial for ensemble performance.

Place, publisher, year, edition, pages
Örebro universitet , 2009. , 106 p.
Series
Studies from the School of Science and Technology at Örebro University, 2
Keyword [en]
Ensemble Learning, Machine Learning, Diversity, Artificial Neural Networks, Data Mining, Information Fusion
National Category
Computer Science
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-2920OAI: oai:DiVA.org:his-2920DiVA: diva2:209912
Presentation
(Swedish)
Available from: 2009-05-07 Created: 2009-03-27 Last updated: 2013-04-16Bibliographically approved

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http://hdl.handle.net/2320/4976

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CiteExportLink to record
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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
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Output format
  • html
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  • asciidoc
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