Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
Zur Ablage hinzufügen
 Lokale TagsFreigabegeschichteDetailsÜbersicht
  Interactive visual labelling versus active learning: an experimental comparison

Chegini, M., Bernard, J., Cui, J., Chegini, F., Sourin, A., Andrews, K., et al. (2020). Interactive visual labelling versus active learning: an experimental comparison. Frontiers of Information Technology & Electronic Engineering, 21, 524-535. doi:10.1631/FITEE.1900549.

Item is

 

einblenden: alle ausblenden: alle

Basisdaten

einblenden: ausblenden:
Datensatz-Permalink: https://hdl.handle.net/21.11116/0000-0006-633F-5 Versions-Permalink: https://hdl.handle.net/21.11116/0000-0007-7938-3
Genre: Zeitschriftenartikel

Externe Referenzen

einblenden:

Urheber

einblenden:
ausblenden:
 Urheber:
Chegini, Mohammad1, Autor
Bernard, Jurgen1, Autor
Cui, Jian1, Autor
Chegini, Fatemeh2, Autor
Sourin, Alexei1, Autor
Andrews, Keith1, Autor
Schreck, Tobias1, Autor
Affiliations:
1external, ou_persistent22              
2Ocean Biogeochemistry, The Ocean in the Earth System, MPI for Meteorology, Max Planck Society, Bundesstraße 53, 20146 Hamburg, DE, ou_913556              

Inhalt

einblenden:
ausblenden:
Schlagwörter: -
 Zusammenfassung: Methods from supervised machine learning allow the classification of new data automatically and are tremendously helpful for data analysis. The quality of supervised maching learning depends not only on the type of algorithm used, but also on the quality of the labelled dataset used to train the classifier. Labelling instances in a training dataset is often done manually relying on selections and annotations by expert analysts, and is often a tedious and time-consuming process. Active learning algorithms can automatically determine a subset of data instances for which labels would provide useful input to the learning process. Interactive visual labelling techniques are a promising alternative, providing effective visual overviews from which an analyst can simultaneously explore data records and select items to a label. By putting the analyst in the loop, higher accuracy can be achieved in the resulting classifier. While initial results of interactive visual labelling techniques are promising in the sense that user labelling can improve supervised learning, many aspects of these techniques are still largely unexplored. This paper presents a study conducted using the mVis tool to compare three interactive visualisations, similarity map, scatterplot matrix (SPLOM), and parallel coordinates, with each other and with active learning for the purpose of labelling a multivariate dataset. The results show that all three interactive visual labelling techniques surpass active learning algorithms in terms of classifier accuracy, and that users subjectively prefer the similarity map over SPLOM and parallel coordinates for labelling. Users also employ different labelling strategies depending on the visualisation used.

Details

einblenden:
ausblenden:
Sprache(n): eng - English
 Datum: 20202020
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1631/FITEE.1900549
 Art des Abschluß: -

Veranstaltung

einblenden:

Entscheidung

einblenden:

Projektinformation

einblenden:

Quelle 1

einblenden:
ausblenden:
Titel: Frontiers of Information Technology & Electronic Engineering
Genre der Quelle: Zeitschrift
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: Springer & Zhejiang University Press
Seiten: - Band / Heft: 21 Artikelnummer: - Start- / Endseite: 524 - 535 Identifikator: ISSN: 2095-9184