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{{PublicationSetupTemplate|Author=Antanas Verikas, Marija Bacauskiene
|PID=235620
|Name=Verikas, Antanas [av] (Högskolan i Halmstad [2804], Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) [3905], Halmstad Embedded and Intelligent Systems Research (EIS) [3938]);Bacauskiene, Marija (Department of Applied Electronics, Kaunas University of Technology, LT-3031, Kaunas, Lithuania)
|Title=Feature Selection with Neural Networks
|PublicationType=Journal Paper
|ContentType=Refereegranskat
|Language=eng
|Journal=Pattern Recognition Letters
|JournalISSN=0167-8655
|Status=published
|Volume=23
|Issue=11
|HostPublication=
|Conference=
|StartPage=1323
|EndPage=1335
|Year=2002
|Edition=
|Pages=
|City=Amsterdam
|Publisher=Elsevier
|Series=
|SeriesISSN=
|ISBN=
|Urls=
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|DOI=http://dx.doi.org/10.1016/S0167-8655(02)00081-8
|ISI=000176094200009
|PMID=
|ScopusId=2-s2.0-0036721934
|NBN=urn:nbn:se:hh:diva-98
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|Keywords=classification;neural network;feature selection;regularization
|Categories=Data- och informationsvetenskap (102)
|ResearchSubjects=
|Projects=
|Notes=
|Abstract=<p>We present a neural network based approach for identifying salient features for classification in feedforward neural networks. Our approach involves neural network training with an augmented cross-entropy error function. The augmented error function forces the neural network to keep low derivatives of the transfer functions of neurons when learning a classification task. Such an approach reduces output sensitivity to the input changes. Feature selection is based on the reaction of the cross-validation data set classification error due to the removal of the individual features. We demonstrate the usefulness of the proposed approach on one artificial and three real-world classification problems. We compared the approach with five other feature selection methods, each of which banks on a different concept. The algorithm developed outperformed the other methods by achieving higher classification accuracy on all the problems tested.</p>
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|CreatedDate=2009-09-17
|PublicationDate=2009-09-17
|LastUpdated=2013-06-14
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 {{PublicationSetupTemplate|Author=Antanas Verikas, Marija Bacauskiene
 |PID=235620
-|Name=Verikas, Antanas [av] (Högskolan i Halmstad [2804], Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) [3905], Halmstad Embedded and Intelligent Systems Research (EIS) [3938]);Bacauskiene, Marija (Department of Applied Electronics, Kaunas University of Technology, LT-3031, Kaunas, Lithuania)
+|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));Bacauskiene, Marija (Department of Applied Electronics, Kaunas University of Technology, LT-3031, Kaunas, Lithuania)
 |Title=Feature Selection with Neural Networks
 |PublicationType=Journal Paper
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 |CreatedDate=2009-09-17
 |PublicationDate=2009-09-17
-|LastUpdated=2013-06-14
+|LastUpdated=2014-11-10
 |diva=http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:235620}}
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Publications:Feature Selection with Neural Networks - Revision history

Slawek at 20:39, 30 September 2016

SlawekBot: Created page with " == Do not edit this section == {{PublicationSetupTemplate|Author=Antanas Verikas, Marija Bacauskiene |PID=235620 |Name=Verikas, Antanas [av]..."

SlawekBot: Created page with "
== Do not edit this section ==
{{PublicationSetupTemplate|Author=Antanas Verikas, Marija Bacauskiene |PID=235620 |Name=Verikas, Antanas [av]..."