Publikation:

Interpreting Fuzzy Models : the Discriminative Power of Input Features

Lade...
Vorschaubild

Dateien

Zu diesem Dokument gibt es keine Dateien.

Datum

1999

Autor:innen

Silipo, Rosaria

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
Core Facility der Universität Konstanz

Gesperrt bis

Titel in einer weiteren Sprache

Publikationstyp
Working Paper/Technical Report
Publikationsstatus
Published

Erschienen in

Zusammenfassung

An important part of the interpretation of a decision process lies on the ascertainment of the influence of the input features, that is, of how much the implemented model relies on a given input feature to perform the desired task. Recently data analysis techniques based on fuzzy logic have gained attention because of their interpretability. Many real-world applications, however, have very high dimensionality and require very complex decision borders. In this case the number of fuzzy rules can proliferate and the easy interpretability of the fuzzy model can progressively disappear.



A method is presented that quantifies the discriminative power of the input features in a fuzzy model. The proposed quantification helps the interpretation of fuzzy models constructed on high dimensional and very fragmented training sets. First, a measure of the information contained in the fuzzy model is defined on the basis of its fuzzy rules. The classification is then performed along one of the input features, that is, the fuzzy rules are split according to that feature's linguistic values. For each linguistic value, a fuzzy sub-model is generated from the original fuzzy model. The average information contained in these fuzzy sub-models is measured and the relative comparison with the information measure of the original fuzzy model quantifies the information gain that derives from the classification performed on the selected input feature. This information gain characterizes the discriminative power of that input feature. Therefore, the proposed information gain can be used to obtain better insights into the selected fuzzy classification strategy, even in very high dimensional cases, and possibly to reduce the input dimension.



Several artificial and real-world data analysis are reported as examples, in order to illustrate the characteristics and potentialities of the proposed algorithm. As real-world examples, the most informative electrocardiographic measures are detected for an arrhythmia classification problem and the role of duration, amplitude and pitch variations of syllabic nuclei in American English spoken sentences is investigated for prosodic stress classification.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
004 Informatik

Schlagwörter

Konferenz

Rezension
undefined / . - undefined, undefined

Forschungsvorhaben

Organisationseinheiten

Zeitschriftenheft

Zugehörige Datensätze in KOPS

Zitieren

ISO 690SILIPO, Rosaria, Michael R. BERTHOLD, 1999. Interpreting Fuzzy Models : the Discriminative Power of Input Features
BibTex
@techreport{Silipo1999Inter-24295,
  year={1999},
  title={Interpreting Fuzzy Models : the Discriminative Power of Input Features},
  author={Silipo, Rosaria and Berthold, Michael R.},
  note={Link zur Originalveröffentlichung: http://techreports.lib.berkeley.edu/accessPages/CSD-99-1079.html}
}
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/123456789/24295">
    <dcterms:issued>1999</dcterms:issued>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2013-08-23T05:59:27Z</dcterms:available>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:creator>Berthold, Michael R.</dc:creator>
    <dc:rights>terms-of-use</dc:rights>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2013-08-23T05:59:27Z</dc:date>
    <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/24295"/>
    <dcterms:title>Interpreting Fuzzy Models : the Discriminative Power of Input Features</dcterms:title>
    <dc:contributor>Silipo, Rosaria</dc:contributor>
    <dc:language>eng</dc:language>
    <dc:contributor>Berthold, Michael R.</dc:contributor>
    <dcterms:abstract xml:lang="eng">An important part of the interpretation of a decision process lies on the ascertainment of the influence of the input features, that is, of how much the implemented model relies on a given input feature to perform the desired task. Recently data analysis techniques based on fuzzy logic have gained attention because of their interpretability. Many real-world applications, however, have very high dimensionality and require very complex decision borders. In this case the number of fuzzy rules can proliferate and the easy interpretability of the fuzzy model can progressively disappear.&lt;br /&gt;&lt;br /&gt;&lt;br /&gt;&lt;br /&gt;A method is presented that quantifies the discriminative power of the input features in a fuzzy model. The proposed quantification helps the interpretation of fuzzy models constructed on high dimensional and very fragmented training sets. First, a measure of the information contained in the fuzzy model is defined on the basis of its fuzzy rules. The classification is then performed along one of the input features, that is, the fuzzy rules are split according to that feature's linguistic values. For each linguistic value, a fuzzy sub-model is generated from the original fuzzy model. The average information contained in these fuzzy sub-models is measured and the relative comparison with the information measure of the original fuzzy model quantifies the information gain that derives from the classification performed on the selected input feature. This information gain characterizes the discriminative power of that input feature. Therefore, the proposed information gain can be used to obtain better insights into the selected fuzzy classification strategy, even in very high dimensional cases, and possibly to reduce the input dimension.&lt;br /&gt;&lt;br /&gt;&lt;br /&gt;&lt;br /&gt;Several artificial and real-world data analysis are reported as examples, in order to illustrate the characteristics and potentialities of the proposed algorithm. As real-world examples, the most informative electrocardiographic measures are detected for an arrhythmia classification problem and the role of duration, amplitude and pitch variations of syllabic nuclei in American English spoken sentences is investigated for prosodic stress classification.</dcterms:abstract>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:creator>Silipo, Rosaria</dc:creator>
    <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/"/>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
  </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

Link zur Originalveröffentlichung: http://techreports.lib.berkeley.edu/accessPages/CSD-99-1079.html
Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Nein
Begutachtet
Diese Publikation teilen