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Comparing Methods for Generating Diverse Ensembles of Artificial Neural Networks
University of Borås, School of Business and IT. (CSL@BS)
University of Borås, School of Business and IT. (CSL@BS)
2010 (English)Conference paper, Published paper (Refereed)
Abstract [en]

It is well-known that ensemble performance relies heavily on sufficient diversity among the base classifiers. With this in mind, the strategy used to balance diversity and base classifier accuracy must be considered a key component of any ensemble algorithm. This study evaluates the predictive performance of neural network ensembles, specifically comparing straightforward techniques to more sophisticated. In particular, the sophisticated methods GASEN and NegBagg are compared to more straightforward methods, where each ensemble member is trained independently of the others. In the experimentation, using 31 publicly available data sets, the straightforward methods clearly outperformed the sophisticated methods, thus questioning the use of the more complex algorithms.

Place, publisher, year, edition, pages
IEEE , 2010.
Series
CFP10IJS-DVD
Keyword [en]
ensembles, diversity, Machine Learning
National Category
Computer Science Computer and Information Science
Identifiers
URN: urn:nbn:se:hb:diva-6145DOI: 10.1109/IJCNN.2010.5596763Local ID: 2320/6869ISBN: 978-1-4244-6916-1 (print)OAI: oai:DiVA.org:hb-6145DiVA: diva2:886829
Conference
WCCI 2010 IEEE World Congress on Computational Intelligence, IJCNN 2010
Available from: 2015-12-22 Created: 2015-12-22 Last updated: 2017-05-02

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fulltext(229 kB)112 downloads
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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