Publikation: Building precise classifiers with automatic rule extraction
Dateien
Datum
Autor:innen
Herausgeber:innen
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
URI (zitierfähiger Link)
DOI (zitierfähiger Link)
Internationale Patentnummer
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Core Facility der Universität Konstanz
Titel in einer weiteren Sprache
Publikationstyp
Publikationsstatus
Erschienen in
Zusammenfassung
An algorithm is presented to train a special kind of a local basis function classifier. The so-called “rectangular basis function network” (RecBFN) consists of hidden units, each covering a rectangular area in the input space, using a trapezoidal activation function. The underlying training algorithm allows easy and fast construction of these types of networks and no parameters need to be adjusted, only normalization of the input-data is necessary. Classification performance of the RecBFN is shown to be comparable to the state of the art classifiers on eight datasets from the StatLog archive. In addition the resulting network allows easy extraction of the learned rules in a form of if-then statements. These rules additionally include soft boundaries resulting in membership values for each class (a possibility of membership is provided). Extraction of meaningful rules is demonstrated on several datasets. The resulting rules can be ranked according to the order of importance and allow the net to extract only few relevant rules in the case of a larger rule base. It is shown that the performance of the network degrades smoothly with the number of rules excluded from the final rule set
Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
Schlagwörter
Konferenz
Rezension
Zitieren
ISO 690
HUBER, Klaus-Peter, Michael R. BERTHOLD, 1995. Building precise classifiers with automatic rule extraction. ICNN'95 - International Conference on Neural Networks. Perth, WA, Australia. In: Proceedings of ICNN'95 - International Conference on Neural Networks. IEEE, 1995, pp. 1263-1268. ISBN 0-7803-2768-3. Available under: doi: 10.1109/ICNN.1995.487337BibTex
@inproceedings{Huber1995Build-24193, year={1995}, doi={10.1109/ICNN.1995.487337}, title={Building precise classifiers with automatic rule extraction}, isbn={0-7803-2768-3}, publisher={IEEE}, booktitle={Proceedings of ICNN'95 - International Conference on Neural Networks}, pages={1263--1268}, author={Huber, Klaus-Peter and Berthold, Michael R.} }
RDF
<rdf:RDF xmlns:dcterms="http://purl.org/dc/terms/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:void="http://rdfs.org/ns/void#" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/24193"> <dcterms:title>Building precise classifiers with automatic rule extraction</dcterms:title> <dc:rights>terms-of-use</dc:rights> <dc:contributor>Berthold, Michael R.</dc:contributor> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2013-08-23T13:07:40Z</dcterms:available> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/24193"/> <dc:creator>Huber, Klaus-Peter</dc:creator> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dc:contributor>Huber, Klaus-Peter</dc:contributor> <dcterms:issued>1995</dcterms:issued> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/> <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/> <dc:language>eng</dc:language> <foaf:homepage rdf:resource="http://localhost:8080/"/> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/> <dcterms:bibliographicCitation>1995 IEEE International Conference on Neural Networks proceedings : Perth, Western Australia, 27 November - 1 December 1995; Vol. 3. - Piscataway, NJ : IEEE Service Center, 1995. - S. 1263-1268. - ISBN 0-7803-2768-3</dcterms:bibliographicCitation> <dc:creator>Berthold, Michael R.</dc:creator> <dcterms:abstract xml:lang="eng">An algorithm is presented to train a special kind of a local basis function classifier. The so-called “rectangular basis function network” (RecBFN) consists of hidden units, each covering a rectangular area in the input space, using a trapezoidal activation function. The underlying training algorithm allows easy and fast construction of these types of networks and no parameters need to be adjusted, only normalization of the input-data is necessary. Classification performance of the RecBFN is shown to be comparable to the state of the art classifiers on eight datasets from the StatLog archive. In addition the resulting network allows easy extraction of the learned rules in a form of if-then statements. These rules additionally include soft boundaries resulting in membership values for each class (a possibility of membership is provided). Extraction of meaningful rules is demonstrated on several datasets. The resulting rules can be ranked according to the order of importance and allow the net to extract only few relevant rules in the case of a larger rule base. It is shown that the performance of the network degrades smoothly with the number of rules excluded from the final rule set</dcterms:abstract> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2013-08-23T13:07:40Z</dc:date> </rdf:Description> </rdf:RDF>