From radial to rectangular basis functions : A new approach for rule learning from large datasets

Lade...
Vorschaubild
Dateien
From radial to rectangular basis.pdf
From radial to rectangular basis.pdfGröße: 283.9 KBDownloads: 298
Datum
1995
Autor:innen
Berthold, Michael R.
Huber, Klaus-Peter
Herausgeber:innen
Kontakt
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
DOI (zitierfähiger Link)
ArXiv-ID
Internationale Patentnummer
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Open Access Green
Core Facility der Universität Konstanz
Gesperrt bis
Titel in einer weiteren Sprache
Publikationstyp
Working Paper/Technical Report
Publikationsstatus
Published
Erschienen in
Zusammenfassung

Automatic extraction of rules from datasets has gained considerable interest during the last few years. Several approaches have been proposed, mainly based on Machine Learning algorithms, the most prominent example being Quinlan's C4.5.



In this paper we propose a new method to find rules in large databases, that make use of so-called Rectangular Basis Functions (or RecBF). Each RecBF directly represents one rule, formulating a condition on all or a subset of all attributes. Because not all attributes have to be used in each rule, rules tend to be less restrictive and result in a more generalizing rule set.



The rule finding mechanism makes use of a slightly modified constructive algorithm already known from Radial Basis Functions. This algorithm allows to generate the `network of rules' on-line. It starts off with large, general rules and specializes them individually, based on conflicts.



In this paper we present the algorithm to construct the rule base, discuss its properties using a few data sets and outline some extensions.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
Konferenz
Rezension
undefined / . - undefined, undefined
Forschungsvorhaben
Organisationseinheiten
Zeitschriftenheft
Datensätze
Zitieren
ISO 690BERTHOLD, Michael R., Klaus-Peter HUBER, 1995. From radial to rectangular basis functions : A new approach for rule learning from large datasets
BibTex
@techreport{Berthold1995radia-24080,
  year={1995},
  title={From radial to rectangular basis functions : A new approach for rule learning from large datasets},
  author={Berthold, Michael R. and Huber, Klaus-Peter},
  note={Interner Bericht // Universität Karlsruhe, Fakultät für Informatik, ISSN 1432-7864 ; 95,15; http://edok01.tib.uni-hannover.de/edoks/e001/249692902.pdf}
}
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/123456789/24080">
    <dc:creator>Berthold, Michael R.</dc:creator>
    <dcterms:issued>1995</dcterms:issued>
    <dc:rights>terms-of-use</dc:rights>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dc:contributor>Huber, Klaus-Peter</dc:contributor>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/24080/1/From%20radial%20to%20rectangular%20basis.pdf"/>
    <dcterms:title>From radial to rectangular basis functions : A new approach for rule learning from large datasets</dcterms:title>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:contributor>Berthold, Michael R.</dc:contributor>
    <dc:language>eng</dc:language>
    <dcterms:abstract xml:lang="eng">Automatic extraction of rules from datasets has gained considerable interest during the last few years. Several approaches have been proposed, mainly based on Machine Learning algorithms, the most prominent example being Quinlan's C4.5.&lt;br /&gt;&lt;br /&gt;&lt;br /&gt;&lt;br /&gt;In this paper we propose a new method to find rules in large databases, that make use of so-called Rectangular Basis Functions (or RecBF). Each RecBF directly represents one rule, formulating a condition on all or a subset of all attributes. Because not all attributes have to be used in each rule, rules tend to be less restrictive and result in a more generalizing rule set.&lt;br /&gt;&lt;br /&gt;&lt;br /&gt;&lt;br /&gt;The rule finding mechanism makes use of a slightly modified constructive algorithm already known from Radial Basis Functions. This algorithm allows to generate the `network of rules' on-line. It starts off with large, general rules and specializes them individually, based on conflicts.&lt;br /&gt;&lt;br /&gt;&lt;br /&gt;&lt;br /&gt;In this paper we present the algorithm to construct the rule base, discuss its properties using a few data sets and outline some extensions.</dcterms:abstract>
    <dc:creator>Huber, Klaus-Peter</dc:creator>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/24080/1/From%20radial%20to%20rectangular%20basis.pdf"/>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2013-08-22T08:57:25Z</dcterms:available>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2013-08-22T08:57:25Z</dc:date>
    <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/24080"/>
  </rdf:Description>
</rdf:RDF>
Interner Vermerk
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Kontakt
URL der Originalveröffentl.
Prüfdatum der URL
Prüfungsdatum der Dissertation
Finanzierungsart
Kommentar zur Publikation
Interner Bericht // Universität Karlsruhe, Fakultät für Informatik, ISSN 1432-7864 ; 95,15; http://edok01.tib.uni-hannover.de/edoks/e001/249692902.pdf
Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Nein
Begutachtet
Diese Publikation teilen