FDive : Learning Relevance Models Using Pattern-based Similarity Measures

Dennig, Frederik L.; Polk, Tom; Lin, Zudi; Schreck, Tobias; Pfister, Hanspeter; Behrisch, Michael

FDive : Learning Relevance Models Using Pattern-based Similarity Measures

Dateien

Zu diesem Dokument gibt es keine Dateien.

Datum

2019

Autor:innen

Dennig, Frederik L.

Polk, Tom

Lin, Zudi

Schreck, Tobias

Pfister, Hanspeter

Behrisch, Michael

DOI (zitierfähiger Link)

https://doi.org/10.1109/VAST47406.2019.8986940

Sammlungen

Informatik und Informationswissenschaft

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.

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

Zitieren

ISO 690

DENNIG, 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}
}

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Universitätsbibliographie

Ja