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Selecting salient features for classification based on neural network committees
Department of Applied Electronics, Kaunas University of Technology LT-3031, Kaunas, Lithuania.
Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).ORCID-id: 0000-0003-2185-8973
2004 (engelsk)Inngår i: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 25, nr 16, s. 1879-1891Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

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.

sted, utgiver, år, opplag, sider
Amsterdam: Elsevier Science , 2004. Vol. 25, nr 16, s. 1879-1891
Emneord [en]
Classification, Decision fusion, Feature selection, Neural network committee
HSV kategori
Identifikatorer
URN: urn:nbn:se:hh:diva-241DOI: 10.1016/j.patrec.2004.08.018ISI: 000225199400010Scopus ID: 2-s2.0-8344257309Lokal ID: 2082/536OAI: oai:DiVA.org:hh-241DiVA, id: diva2:237419
Tilgjengelig fra: 2006-11-24 Laget: 2006-11-24 Sist oppdatert: 2017-12-13bibliografisk kontrollert

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