Discriminative Power of Input Features in a Fuzzy Model


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SILIPO, Rosaria, Michael R. BERTHOLD, 1999. Discriminative Power of Input Features in a Fuzzy Model. In: HAND, David J., ed., Joost N. KOK, ed., Michael R. BERTHOLD, ed.. Advances in Intelligent Data Analysis. Berlin, Heidelberg:Springer Berlin Heidelberg, pp. 87-98. ISBN 978-3-540-66332-4

@inproceedings{Silipo1999-07-08Discr-24076, title={Discriminative Power of Input Features in a Fuzzy Model}, year={1999}, doi={10.1007/3-540-48412-4_8}, number={1642}, isbn={978-3-540-66332-4}, address={Berlin, Heidelberg}, publisher={Springer Berlin Heidelberg}, series={Lecture Notes in Computer Science}, booktitle={Advances in Intelligent Data Analysis}, pages={87--98}, editor={Hand, David J. and Kok, Joost N. and Berthold, Michael R.}, author={Silipo, Rosaria and Berthold, Michael R.} }

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/24076"> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/24076"/> <dc:contributor>Silipo, Rosaria</dc:contributor> <dcterms:issued>1999-07-08</dcterms:issued> <dc:contributor>Berthold, Michael R.</dc:contributor> <dc:rights>deposit-license</dc:rights> <dc:creator>Berthold, Michael R.</dc:creator> <dcterms:abstract xml:lang="eng">In many modern data analysis scenarios the first and most urgent task consists of reducing the redundancy in high dimensional input spaces. A method is presented that quantifies the discriminative power of the input features in a fuzzy model. A possibilistic information measure of the model is defined on the basis of the available fuzzy rules and the resulting possibilistic information gain, associated with the use of a given input dimension, characterizes the input feature’s discriminative power. Due to the low computational expenses derived from the use of a fuzzy model, the proposed possibilistic information gain generates a simple and efficient algorithm for the reduction of the input dimensionality, even for high dimensional cases. As real-world example, the most informative electrocardiographic measures are detected for an arrhythmia classification problem.</dcterms:abstract> <dc:language>eng</dc:language> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2013-07-26T07:39:31Z</dc:date> <dc:creator>Silipo, Rosaria</dc:creator> <dcterms:title>Discriminative Power of Input Features in a Fuzzy Model</dcterms:title> <dcterms:bibliographicCitation>Advances in intelligent data analysis : third International Symposium, IDA-99, Amsterdam, The Netherlands, August 9 - 11, 1999; proceedings / David J. Hand ... (eds.). - Berlin [u.a.] : Springer, 1999. - S. 87-98. - (Lecture notes in computer science ; 1642). - ISBN 3-540-66332-0</dcterms:bibliographicCitation> <dcterms:rights rdf:resource="http://nbn-resolving.org/urn:nbn:de:bsz:352-20140905103605204-4002607-1"/> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2013-07-26T07:39:31Z</dcterms:available> </rdf:Description> </rdf:RDF>

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