Task-based Visual Interactive Modeling : Decision Trees and Rule-based Classifiers

Lade...
Vorschaubild
Dateien
Streeb_2-1smjgfpk1i8ax9.pdf
Streeb_2-1smjgfpk1i8ax9.pdfGröße: 9.44 MBDownloads: 787
Datum
2021
Herausgeber:innen
Kontakt
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
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
Zeitschriftenartikel
Publikationsstatus
Published
Erschienen in
IEEE Transactions on Visualization and Computer Graphics (T-VCG). IEEE. ISSN 1077-2626. eISSN 1941-0506. Available under: doi: 10.1109/TVCG.2020.3045560
Zusammenfassung

Visual analytics enables the coupling of machine learning models and humans in a tightly integrated workflow, addressing various analysis tasks. Each task poses distinct demands to analysts and decision-makers. In this survey, we focus on one canonical technique for rule-based classification, namely decision tree classifiers. We provide an overview of available visualizations for decision trees with a focus on how visualizations differ with respect to 16 tasks. Further, we investigate the types of visual designs employed, and the quality measures presented. We find that (i) interactive visual analytics systems for classifier development offer a variety of visual designs, (ii) utilization tasks are sparsely covered, (iii) beyond classifier development, node-link diagrams are omnipresent, (iv) even systems designed for machine learning experts rarely feature visual representations of quality measures other than accuracy. In conclusion, we see a potential for integrating algorithmic techniques, mathematical quality measures, and tailored interactive visualizations to enable human experts to utilize their knowledge more effectively.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
Decision Trees, Rule-based Classification, Visual Analytics, Interactive Machine Learning, Interactive Model Analysis, Survey, Visualization
Konferenz
Rezension
undefined / . - undefined, undefined
Forschungsvorhaben
Organisationseinheiten
Zeitschriftenheft
Datensätze
Zitieren
ISO 690STREEB, Dirk, Yannick METZ, Udo SCHLEGEL, Bruno SCHNEIDER, Mennatallah EL-ASSADY, Hansjörg NETH, Min CHEN, Daniel A. KEIM, 2021. Task-based Visual Interactive Modeling : Decision Trees and Rule-based Classifiers. In: IEEE Transactions on Visualization and Computer Graphics (T-VCG). IEEE. ISSN 1077-2626. eISSN 1941-0506. Available under: doi: 10.1109/TVCG.2020.3045560
BibTex
@article{Streeb2021-01-13Taskb-53075,
  year={2021},
  doi={10.1109/TVCG.2020.3045560},
  title={Task-based Visual Interactive Modeling : Decision Trees and Rule-based Classifiers},
  issn={1077-2626},
  journal={IEEE Transactions on Visualization and Computer Graphics (T-VCG)},
  author={Streeb, Dirk and Metz, Yannick and Schlegel, Udo and Schneider, Bruno and El-Assady, Mennatallah and Neth, Hansjörg and Chen, Min and Keim, Daniel A.}
}
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/123456789/53075">
    <dc:contributor>Schlegel, Udo</dc:contributor>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43"/>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43"/>
    <dc:rights>terms-of-use</dc:rights>
    <dc:contributor>Streeb, Dirk</dc:contributor>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/53075/1/Streeb_2-1smjgfpk1i8ax9.pdf"/>
    <dc:contributor>El-Assady, Mennatallah</dc:contributor>
    <dc:language>eng</dc:language>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:contributor>Metz, Yannick</dc:contributor>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-03-05T08:52:26Z</dc:date>
    <dcterms:title>Task-based Visual Interactive Modeling : Decision Trees and Rule-based Classifiers</dcterms:title>
    <dc:creator>Metz, Yannick</dc:creator>
    <dc:creator>Chen, Min</dc:creator>
    <dc:creator>Neth, Hansjörg</dc:creator>
    <dc:creator>Streeb, Dirk</dc:creator>
    <dc:contributor>Schneider, Bruno</dc:contributor>
    <dc:contributor>Neth, Hansjörg</dc:contributor>
    <dcterms:issued>2021-01-13</dcterms:issued>
    <dc:creator>El-Assady, Mennatallah</dc:creator>
    <dc:creator>Keim, Daniel A.</dc:creator>
    <dc:creator>Schlegel, Udo</dc:creator>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/53075/1/Streeb_2-1smjgfpk1i8ax9.pdf"/>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-03-05T08:52:26Z</dcterms:available>
    <dc:creator>Schneider, Bruno</dc:creator>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:contributor>Chen, Min</dc:contributor>
    <dcterms:abstract xml:lang="eng">Visual analytics enables the coupling of machine learning models and humans in a tightly integrated workflow, addressing various analysis tasks. Each task poses distinct demands to analysts and decision-makers. In this survey, we focus on one canonical technique for rule-based classification, namely decision tree classifiers. We provide an overview of available visualizations for decision trees with a focus on how visualizations differ with respect to 16 tasks. Further, we investigate the types of visual designs employed, and the quality measures presented. We find that (i) interactive visual analytics systems for classifier development offer a variety of visual designs, (ii) utilization tasks are sparsely covered, (iii) beyond classifier development, node-link diagrams are omnipresent, (iv) even systems designed for machine learning experts rarely feature visual representations of quality measures other than accuracy. In conclusion, we see a potential for integrating algorithmic techniques, mathematical quality measures, and tailored interactive visualizations to enable human experts to utilize their knowledge more effectively.</dcterms:abstract>
    <dc:contributor>Keim, Daniel A.</dc:contributor>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/53075"/>
  </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
Ja
Diese Publikation teilen

Versionsgeschichte

Gerade angezeigt 1 - 1 von 1
VersionDatumZusammenfassung
1*
2021-03-05 08:52:26
* Ausgewählte Version