Learning Fuzzy Models and Potential Outliers


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BERTHOLD, Michael, 2000. Learning Fuzzy Models and Potential Outliers. In: GIACOMO, , ed., Riccia RUDOLF KRUSE, ed., Hans-Joachim LENZ, ed.. Computational intelligence in data mining. Wien [u.a.]:Springer, pp. 111-126. ISBN 3-211-83326-9

@incollection{Berthold2000Learn-24370, title={Learning Fuzzy Models and Potential Outliers}, year={2000}, number={408}, isbn={3-211-83326-9}, address={Wien [u.a.]}, publisher={Springer}, series={International Centre for Mechanical Sciences : Courses and lectures}, booktitle={Computational intelligence in data mining}, pages={111--126}, editor={Giacomo and Rudolf Kruse, Riccia and Lenz, Hans-Joachim}, author={Berthold, Michael} }

<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/123456789/24370"> <dcterms:abstract xml:lang="eng">Outliers or distorted attributes very often severely interfere with data analysis algorithms that try to extract few meaningful rules. Most methods to deal with outliers try to completely ignore them. This can be potentially harmful since the very outlier that was ignored might have described a rare but still extremely interesting phenomena. In this paper we describe an approach that tries to build an interpretable model while still maintaining all the information in the data. This is achieved through a two stage process. A first phase builds an outlier-model for data points of low relevance, followed by a second stage which uses this model as filter and generates a simpler model, describing only examples with higher relevance, thus representing a more general concept. The outlier-model on the other hand may point out potential areas of interest to the user. Preliminary experiments indicate that the two models in fact have lower complexity and sometimes even offer superior performance.</dcterms:abstract> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2013-09-11T09:27:43Z</dcterms:available> <dcterms:bibliographicCitation>Computational intelligence in data mining / Giacomo della Riccia, Rudolf Kruse, Hans-Joachim Lenz (eds.). - Wien [u.a.] : Springer, 2000. - S. 111-126. - (International Centre for Mechanical Sciences: Courses and lectures ; 408). - ISBN 3-211-83326-9</dcterms:bibliographicCitation> <dc:rights>deposit-license</dc:rights> <dcterms:rights rdf:resource="http://nbn-resolving.org/urn:nbn:de:bsz:352-20140905103605204-4002607-1"/> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/24370"/> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2013-09-11T09:27:43Z</dc:date> <dcterms:issued>2000</dcterms:issued> <dc:creator>Berthold, Michael</dc:creator> <dc:language>eng</dc:language> <dcterms:title>Learning Fuzzy Models and Potential Outliers</dcterms:title> <dc:contributor>Berthold, Michael</dc:contributor> </rdf:Description> </rdf:RDF>

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