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Comparing Methods for Generating Diverse Ensembles of Artificial Neural Networks
Högskolan i Borås, Institutionen Handels- och IT-högskolan.
Högskolan i Borås, Institutionen Handels- och IT-högskolan.
Stockholm University, Sweden.
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 Sciences Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-221583DOI: 10.1109/IJCNN.2010.5596763ISI: 000287421403133Scopus ID: 2-s2.0-79959438170Local ID: 2320/6869ISBN: 978-1-4244-6916-1 (print)OAI: oai:DiVA.org:kth-221583DiVA, id: diva2:1175220
Conference
WCCI 2010 IEEE World Congress on Computational Intelligence, IJCNN 2010
Note

QC 20180206

Available from: 2018-01-17 Created: 2018-01-17 Last updated: 2018-02-06Bibliographically approved

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
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  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NO
  • nn-NB
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More languages
Output format
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