Difference between revisions of "Publications:Selecting salient features for classification based on neural network committees"

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|Name=Bacauskiene, Marija (Högskolan i Halmstad [2804], Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) [3905]);Verikas, Antanas [av] (Högskolan i Halmstad [2804], Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) [3905], Halmstad Embedded and Intelligent Systems Research (EIS) [3938])
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|Name=Bacauskiene, Marija (Department of Applied Electronics, Kaunas University of Technology LT-3031, Kaunas, Lithuania);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))
 
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Title Selecting salient features for classification based on neural network committees
Author Marija Bacauskiene and Antanas Verikas
Year 2004
PublicationType Journal Paper
Journal Pattern Recognition Letters
HostPublication
Conference
DOI http://dx.doi.org/10.1016/j.patrec.2004.08.018
Diva url http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:237419
Abstract Aggregating outputs of multiple classifiers into a committee decision is one of the most important techniques for improving classification accuracy. The issue of selecting an optimal subset of relevant features plays also an important role in successful design of a pattern recognition system. In this paper, we present a neural network based approach for identifying salient features for classification in neural network committees. Feature selection is based on two criteria, namely the reaction of the cross-validation data set classification error due to the removal of the individual features and the diversity of neural networks comprising the committee. The algorithm developed removed a large number of features from the original data sets without reducing the classification accuracy of the committees. The accuracy of the committees utilizing the reduced feature sets was higher than those exploiting all the original features.