Publikation:

Integrating Data and Model Space in Ensemble Learning by Visual Analytics

Lade...
Vorschaubild

Dateien

Schneider_2-1u03go1k159id1.pdf
Schneider_2-1u03go1k159id1.pdfGröße: 3.3 MBDownloads: 326

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 Big Data. Institute of Electrical and Electronics Engineers (IEEE). 2021, 7(3), pp. 483-496. ISSN 2372-2096. eISSN 2332-7790. Available under: doi: 10.1109/TBDATA.2018.2877350

Zusammenfassung

Ensembles of classifier models typically deliver superior performance and can outperform single classifier models given a dataset and classification task at hand. However, the gain in performance comes together with the lack of comprehensibility, posing a challenge to understand how each model affects the classification outputs and from where the errors come. We propose a tight visual integration of the data and the model space for exploring and combining classifier models. We introduce an interactive workflow that builds upon the visual integration and enables the effective exploration of classification outputs and models. The involvement of the user is key to our approach. Therefore, we elaborate on the role of the human and connect our approach to theoretical frameworks on human-centered machine learning. We showcase the usefulness of our approach and the integration of the user via binary and multiclass classification problems. Based on ensembles automatically selected by a standard ensemble selection algorithm, the user can manipulate models and alternative combinations.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
004 Informatik

Schlagwörter

Classification, Ensemble learning, Data visualization, Graphical user interfaces

Konferenz

Rezension
undefined / . - undefined, undefined

Forschungsvorhaben

Organisationseinheiten

Zeitschriftenheft

Zugehörige Datensätze in KOPS

Zitieren

ISO 690SCHNEIDER, Bruno, Dominik JÄCKLE, Florian STOFFEL, Alexandra DIEHL, Johannes FUCHS, Daniel A. KEIM, 2021. Integrating Data and Model Space in Ensemble Learning by Visual Analytics. In: IEEE Transactions on Big Data. Institute of Electrical and Electronics Engineers (IEEE). 2021, 7(3), pp. 483-496. ISSN 2372-2096. eISSN 2332-7790. Available under: doi: 10.1109/TBDATA.2018.2877350
BibTex
@article{Schneider2021Integ-44388,
  year={2021},
  doi={10.1109/TBDATA.2018.2877350},
  title={Integrating Data and Model Space in Ensemble Learning by Visual Analytics},
  number={3},
  volume={7},
  issn={2372-2096},
  journal={IEEE Transactions on Big Data},
  pages={483--496},
  author={Schneider, Bruno and Jäckle, Dominik and Stoffel, Florian and Diehl, Alexandra and Fuchs, Johannes 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/123456789/44388">
    <dc:contributor>Fuchs, Johannes</dc:contributor>
    <dc:creator>Schneider, Bruno</dc:creator>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dc:creator>Fuchs, Johannes</dc:creator>
    <dcterms:abstract xml:lang="eng">Ensembles of classifier models typically deliver superior performance and can outperform single classifier models given a dataset and classification task at hand. However, the gain in performance comes together with the lack of comprehensibility, posing a challenge to understand how each model affects the classification outputs and from where the errors come. We propose a tight visual integration of the data and the model space for exploring and combining classifier models. We introduce an interactive workflow that builds upon the visual integration and enables the effective exploration of classification outputs and models. The involvement of the user is key to our approach. Therefore, we elaborate on the role of the human and connect our approach to theoretical frameworks on human-centered machine learning. We showcase the usefulness of our approach and the integration of the user via binary and multiclass classification problems. Based on ensembles automatically selected by a standard ensemble selection algorithm, the user can manipulate models and alternative combinations.</dcterms:abstract>
    <dc:creator>Jäckle, Dominik</dc:creator>
    <dcterms:title>Integrating Data and Model Space in Ensemble Learning by Visual Analytics</dcterms:title>
    <dc:contributor>Schneider, Bruno</dc:contributor>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/44388"/>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:creator>Diehl, Alexandra</dc:creator>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2018-12-19T13:38:29Z</dc:date>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/44388/1/Schneider_2-1u03go1k159id1.pdf"/>
    <dc:contributor>Diehl, Alexandra</dc:contributor>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2018-12-19T13:38:29Z</dcterms:available>
    <dc:creator>Keim, Daniel A.</dc:creator>
    <dc:creator>Stoffel, Florian</dc:creator>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/44388/1/Schneider_2-1u03go1k159id1.pdf"/>
    <dc:language>eng</dc:language>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:contributor>Jäckle, Dominik</dc:contributor>
    <dcterms:issued>2021</dcterms:issued>
    <dc:contributor>Stoffel, Florian</dc:contributor>
    <dc:rights>terms-of-use</dc:rights>
    <dc:contributor>Keim, Daniel A.</dc:contributor>
  </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
Unbekannt
Diese Publikation teilen