View source for Publications:Fusing neural networks through space partitioning and fuzzy integration

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{{PublicationSetupTemplate|Author=Antanas Verikas, Arunas Lipnickas
|PID=285845
|Name=Verikas, Antanas (av) (0000-0003-2185-8973) (Högskolan i Halmstad (2804), Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) (3905), Halmstad Embedded and Intelligent Systems Research (EIS) (3938));Lipnickas, Arunas (Department of Applied Electronics, Kaunas University of Technology, Lithuania)
|Title=Fusing neural networks through space partitioning and fuzzy integration
|PublicationType=Journal Paper
|ContentType=Refereegranskat
|Language=eng
|Journal=Neural Processing Letters
|JournalISSN=1370-4621
|Status=published
|Volume=16
|Issue=1
|HostPublication=
|Conference=
|StartPage=53
|EndPage=65
|Year=2002
|Edition=
|Pages=
|City=New York
|Publisher=Springer
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|DOI=http://dx.doi.org/10.1023/A:1019703911322
|ISI=000177380600005
|PMID=
|ScopusId=2-s2.0-0036671543
|NBN=urn:nbn:se:hh:diva-3540
|LocalId=
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|Keywords=Decision fusion;Fuzzy integral;Half & Half bagging;Neural network;Multiple classifiers;Classification;Recognition;Combination
|Categories=Teknik och teknologier (2)
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|Abstract=<p>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.</p>
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|CreatedDate=2009-12-01
|PublicationDate=2010-01-13
|LastUpdated=2014-11-10
|diva=http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:285845}}
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