Adaptive prototype-based fuzzy classification

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CEBRON, Nicolas, Michael R. BERTHOLD, 2008. Adaptive prototype-based fuzzy classification. In: Fuzzy Sets and Systems. 159(21), pp. 2806-2818

@article{Cebron2008Adapt-2977, title={Adaptive prototype-based fuzzy classification}, year={2008}, doi={10.1016/j.fss.2008.03.019}, number={21}, volume={159}, journal={Fuzzy Sets and Systems}, pages={2806--2818}, author={Cebron, Nicolas 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/123456789/2977"> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-03-23T10:15:34Z</dcterms:available> <dcterms:bibliographicCitation>Publ. in: Fuzzy Sets and Systems, 159 (2008), 21, pp. 2806-2818</dcterms:bibliographicCitation> <dcterms:rights rdf:resource="http://nbn-resolving.org/urn:nbn:de:bsz:352-20140905103416863-3868037-7"/> <dc:creator>Berthold, Michael R.</dc:creator> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/2977"/> <dcterms:issued>2008</dcterms:issued> <dc:language>eng</dc:language> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-03-23T10:15:34Z</dc:date> <dc:contributor>Berthold, Michael R.</dc:contributor> <dcterms:title>Adaptive prototype-based fuzzy classification</dcterms:title> <dc:rights>deposit-license</dc:rights> <dc:contributor>Cebron, Nicolas</dc:contributor> <dc:creator>Cebron, Nicolas</dc:creator> <dcterms:abstract xml:lang="eng">Classifying large datasets without any a priori information poses a problem especially in the field of bioinformatics. In this work, we explore the problem of classifying hundreds of thousands of cell assay images obtained by a high-throughput screening camera. The goal is to label a few selected examples by hand and to automatically label the rest of the images afterwards. Up to now, such images are classified by scripts and classification techniques that are designed to tackle a specific problem. We propose a new adaptive active clustering scheme, based on an initial fuzzy c-means clustering and learning vector quantization. This scheme can initially cluster large datasets unsupervised and then allows for adjustment of the classification by the user. Motivated by the concept of active learning, the learner tries to query the most ''useful'' examples in the learning process and therefore keeps the costs for supervision at a low level. A framework for the classification of cell assay images based on this technique is introduced. We compare our approach to other related techniques in this field based on several datasets.</dcterms:abstract> </rdf:Description> </rdf:RDF>

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