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Fusing neural networks through space partitioning and fuzzy integration
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).ORCID iD: 0000-0003-2185-8973
Department of Applied Electronics, Kaunas University of Technology, Lithuania.
2002 (English)In: Neural Processing Letters, ISSN 1370-4621, E-ISSN 1573-773X, Vol. 16, no 1, p. 53-65Article in journal (Refereed) Published
Abstract [en]

To improve recognition results, decisions of multiple neural networks can be aggregated into a committee decision. Aggregation weights assigned to neural networks or groups of networks can be the same in the entire data space or can be different (data dependent) in various regions of the space. In this paper, we propose a method for obtaining data dependent aggregation weights. The proposed approach is tested in two aggregation schemes, namely aggregation through neural network selection, and aggregation by the Choquet integral with respect to the lambda-fuzzy measure. The effectiveness of the approach is demonstrated on two artificial and three real data sets.

Place, publisher, year, edition, pages
New York: Springer, 2002. Vol. 16, no 1, p. 53-65
Keywords [en]
Decision fusion, Fuzzy integral, Half & Half bagging, Neural network, Multiple classifiers, Classification, Recognition, Combination
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:hh:diva-3540DOI: 10.1023/A:1019703911322ISI: 000177380600005Scopus ID: 2-s2.0-0036671543OAI: oai:DiVA.org:hh-3540DiVA, id: diva2:285845
Available from: 2010-01-13 Created: 2009-12-01 Last updated: 2017-12-12Bibliographically approved

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Verikas, Antanas

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Citation style
  • apa
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  • vancouver
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  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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