FDive : Learning Relevance Models Using Pattern-based Similarity Measures

Lade...
Vorschaubild
Dateien
Zu diesem Dokument gibt es keine Dateien.
Datum
2019
Autor:innen
Lin, Zudi
Schreck, Tobias
Pfister, Hanspeter
Behrisch, Michael
Herausgeber:innen
Kontakt
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
URI (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
Forschungsvorhaben
Organisationseinheiten
Zeitschriftenheft
Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published
Erschienen in
2019 IEEE Conference on Visual Analytics Science and Technology (VAST). Piscataway, NJ: IEEE, 2019, pp. 69-80. ISBN 978-1-72812-284-7. Available under: doi: 10.1109/VAST47406.2019.8986940
Zusammenfassung

The detection of interesting patterns in large high-dimensional datasets is difficult because of their dimensionality and pattern complexity. Therefore, analysts require automated support for the extraction of relevant patterns. In this paper, we present FDive, a visual active learning system that helps to create visually explorable relevance models, assisted by learning a pattern-based similarity. We use a small set of user-provided labels to rank similarity measures, consisting of feature descriptor and distance function combinations, by their ability to distinguish relevant from irrelevant data. Based on the best-ranked similarity measure, the system calculates an interactive Self-Organizing Map-based relevance model, which classifies data according to the cluster affiliation. It also automatically prompts further relevance feedback to improve its accuracy. Uncertain areas, especially near the decision boundaries, are highlighted and can be refined by the user. We evaluate our approach by comparison to state-of-the-art feature selection techniques and demonstrate the usefulness of our approach by a case study classifying electron microscopy images of brain cells. The results show that FDive enhances both the quality and understanding of relevance models and can thus lead to new insights for brain research.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
Visual analytics, similarity measure selection, relevance feedback, active learning, self-organizing maps
Konferenz
2019 IEEE Conference on Visual Analytics Science and Technology (VAST), 20. Okt. 2019 - 25. Okt. 2019, Vancouver, BC, Canada
Rezension
undefined / . - undefined, undefined
Zitieren
ISO 690DENNIG, Frederik L., Tom POLK, Zudi LIN, Tobias SCHRECK, Hanspeter PFISTER, Michael BEHRISCH, 2019. FDive : Learning Relevance Models Using Pattern-based Similarity Measures. 2019 IEEE Conference on Visual Analytics Science and Technology (VAST). Vancouver, BC, Canada, 20. Okt. 2019 - 25. Okt. 2019. In: 2019 IEEE Conference on Visual Analytics Science and Technology (VAST). Piscataway, NJ: IEEE, 2019, pp. 69-80. ISBN 978-1-72812-284-7. Available under: doi: 10.1109/VAST47406.2019.8986940
BibTex
@inproceedings{Dennig2019FDive-53222,
  year={2019},
  doi={10.1109/VAST47406.2019.8986940},
  title={FDive : Learning Relevance Models Using Pattern-based Similarity Measures},
  isbn={978-1-72812-284-7},
  publisher={IEEE},
  address={Piscataway, NJ},
  booktitle={2019 IEEE Conference on Visual Analytics Science and Technology (VAST)},
  pages={69--80},
  author={Dennig, Frederik L. and Polk, Tom and Lin, Zudi and Schreck, Tobias and Pfister, Hanspeter and Behrisch, Michael}
}
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/123456789/53222">
    <dc:contributor>Lin, Zudi</dc:contributor>
    <dc:creator>Behrisch, Michael</dc:creator>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-03-22T15:59:20Z</dcterms:available>
    <dc:contributor>Dennig, Frederik L.</dc:contributor>
    <dc:creator>Polk, Tom</dc:creator>
    <dcterms:abstract xml:lang="eng">The detection of interesting patterns in large high-dimensional datasets is difficult because of their dimensionality and pattern complexity. Therefore, analysts require automated support for the extraction of relevant patterns. In this paper, we present FDive, a visual active learning system that helps to create visually explorable relevance models, assisted by learning a pattern-based similarity. We use a small set of user-provided labels to rank similarity measures, consisting of feature descriptor and distance function combinations, by their ability to distinguish relevant from irrelevant data. Based on the best-ranked similarity measure, the system calculates an interactive Self-Organizing Map-based relevance model, which classifies data according to the cluster affiliation. It also automatically prompts further relevance feedback to improve its accuracy. Uncertain areas, especially near the decision boundaries, are highlighted and can be refined by the user. We evaluate our approach by comparison to state-of-the-art feature selection techniques and demonstrate the usefulness of our approach by a case study classifying electron microscopy images of brain cells. The results show that FDive enhances both the quality and understanding of relevance models and can thus lead to new insights for brain research.</dcterms:abstract>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:contributor>Polk, Tom</dc:contributor>
    <dcterms:issued>2019</dcterms:issued>
    <dcterms:title>FDive : Learning Relevance Models Using Pattern-based Similarity Measures</dcterms:title>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dc:contributor>Schreck, Tobias</dc:contributor>
    <dc:contributor>Behrisch, Michael</dc:contributor>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:creator>Pfister, Hanspeter</dc:creator>
    <dc:creator>Schreck, Tobias</dc:creator>
    <dc:contributor>Pfister, Hanspeter</dc:contributor>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-03-22T15:59:20Z</dc:date>
    <dc:language>eng</dc:language>
    <dc:creator>Lin, Zudi</dc:creator>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/53222"/>
    <dc:creator>Dennig, Frederik L.</dc:creator>
  </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
Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet
Diese Publikation teilen