Chegini, Mohammad ; Bernard, Jürgen ; Shao, Lin ; Sourin, Alexei ; Andrews, Keith ; Schreck, Tobias (2019)
mVis in the Wild: Pre-Study of an Interactive Visual Machine
Learning System for Labelling.
IEEE VIS 2019 Workshop on Evaluation of Interactive Visual Machine Learning Systems (EVIVA-ML). Vancouver, Canada (21.10.2019-21.10.2019)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Many machine learning algorithms require a labelled training dataset. The task of labelling a multivariate dataset can be tedious, but can be supported by systems combining interactive visualisation and machine learning techniques into a single interface. mVis is such a system, providing a unified ecosystem to explore multivariate datasets and execute machine learning algorithms to build labelled datasets. This paper describes a pre-study evaluation of the mVis system, comprising case studies in two different domains: collaborative intelligence and daily activities. In each case study, a volunteer researcher was asked to use mVis to explore, analyse, and label their own dataset in their own environment, while thinking out loud. The case studies provided valuable leanings in terms of the usability of the system, understanding how different analysts work, and identifying important missing features.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2019 |
| Autor(en): | Chegini, Mohammad ; Bernard, Jürgen ; Shao, Lin ; Sourin, Alexei ; Andrews, Keith ; Schreck, Tobias |
| Art des Eintrags: | Bibliographie |
| Titel: | mVis in the Wild: Pre-Study of an Interactive Visual Machine Learning System for Labelling |
| Sprache: | Englisch |
| Publikationsjahr: | 2019 |
| Verlag: | IEEE |
| Veranstaltungstitel: | IEEE VIS 2019 Workshop on Evaluation of Interactive Visual Machine Learning Systems (EVIVA-ML) |
| Veranstaltungsort: | Vancouver, Canada |
| Veranstaltungsdatum: | 21.10.2019-21.10.2019 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | Many machine learning algorithms require a labelled training dataset. The task of labelling a multivariate dataset can be tedious, but can be supported by systems combining interactive visualisation and machine learning techniques into a single interface. mVis is such a system, providing a unified ecosystem to explore multivariate datasets and execute machine learning algorithms to build labelled datasets. This paper describes a pre-study evaluation of the mVis system, comprising case studies in two different domains: collaborative intelligence and daily activities. In each case study, a volunteer researcher was asked to use mVis to explore, analyse, and label their own dataset in their own environment, while thinking out loud. The case studies provided valuable leanings in terms of the usability of the system, understanding how different analysts work, and identifying important missing features. |
| Freie Schlagworte: | Human-centered computing, Visualization |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 27 Aug 2020 08:51 |
| Letzte Änderung: | 27 Aug 2020 08:51 |
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