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Selecting salient features for classification committees
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0003-2185-8973
Department of Applied Electronics, Kaunas University of Technology, Studentu 50, LT-3031, Kaunas, Lithuania.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
2003 (English)In: Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003 / [ed] Kaynak, O Alpaydin, E Oja, E Xu, L, Heidelberg: Springer Berlin/Heidelberg, 2003, Vol. 2714, p. 35-42Conference paper, Published paper (Refereed)
Abstract [en]

We present a neural network based approach for identifying salient features for classification in neural network committees. 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 of the network when learning a classification task. 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. By contrast, the accuracy of the committees utilizing the reduced feature sets was higher than those exploiting all the original features. © Springer-Verlag Berlin Heidelberg 2003.

Place, publisher, year, edition, pages
Heidelberg: Springer Berlin/Heidelberg, 2003. Vol. 2714, p. 35-42
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 2714
Keyword [en]
Errors, Neural networks
National Category
Bioinformatics (Computational Biology) Telecommunications Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:hh:diva-35784DOI: 10.1007/3-540-44989-2_5ISI: 000185378100005Scopus ID: 2-s2.0-21144434169ISBN: 978-3-540-40408-8 (print)ISBN: 978-3-540-44989-8 (electronic)OAI: oai:DiVA.org:hh-35784DiVA, id: diva2:1195746
Conference
Joint International Conference on Artificial Neural Networks (ICANN)/International on Neural Information Processing (ICONIP), JUN 26-29, 2002, ISTANBUL, TURKEY
Available from: 2018-04-06 Created: 2018-04-06 Last updated: 2018-04-06Bibliographically approved

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Verikas, AntanasMalmqvist, Kerstin

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Bioinformatics (Computational Biology)TelecommunicationsBioinformatics and Systems Biology

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