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The Importance of Diversity in Neural Network Ensembles: An Empirical Investigation
School of Business and Informatics, University of Borås, Sweden.
School of Business and Informatics, University of Borås, Sweden.
University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. (Skövde Cognition and Artificial Intelligence Lab (SCAI))
2007 (English)In: IJCNN 2007 Conference Proceedings: The 2007 International Joint Conference on Neural Networks, IEEE, 2007, p. 661-666Conference paper, Published paper (Refereed)
Abstract [en]

When designing ensembles, it is almost an axiom that the base classifiers must be diverse in order for the ensemble to generalize well. Unfortunately, there is no clear definition of the key term diversity, leading to several diversity measures and many, more or less ad hoc, methods for diversity creation in ensembles. In addition, no specific diversity measure has shown to have a high correlation with test set accuracy. The purpose of this paper is to empirically evaluate ten different diversity measures, using neural network ensembles and 11 publicly available data sets. The main result is that all diversity measures evaluated, in this study too, show low or very low correlation with test set accuracy. Having said that, two measures; double fault and difficulty show slightly higher correlations compared to the other measures. The study furthermore shows that the correlation between accuracy measured on training or validation data and test set accuracy also is rather low. These results challenge ensemble design techniques where diversity is explicitly maximized or where ensemble accuracy on a hold-out set is used for optimization.

Place, publisher, year, edition, pages
IEEE, 2007. p. 661-666
Series
Proceedings of the International Joint Conference on Neural Networks, ISSN 2161-4393, E-ISSN 2161-4407
National Category
Computer Sciences Information Systems
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-2103DOI: 10.1109/IJCNN.2007.4371035ISI: 000254291100115Scopus ID: 2-s2.0-44649141743ISBN: 978-1-4244-1379-9 (print)ISBN: 978-1-4244-1380-5 (electronic)ISBN: 1-4244-1380-X (print)ISBN: 1-4244-1380-X (electronic)OAI: oai:DiVA.org:his-2103DiVA, id: diva2:32379
Conference
International Joint Conference on Neural Networks (IJCNN), Orlando, Florida, USA, August 12-17, 2007
Note

IEEE International Conference on Neural Networks, ISSN 1098-7576

©2007 IEEE

Available from: 2008-05-30 Created: 2008-05-30 Last updated: 2021-04-22Bibliographically approved

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Niklasson, Lars

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Citation style
  • apa
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  • Other locale
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Output format
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