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.)
Conference or Workshop Item
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.
| Item Type: | Conference or Workshop Item |
|---|---|
| Erschienen: | 2019 |
| Creators: | Chegini, Mohammad ; Bernard, Jürgen ; Shao, Lin ; Sourin, Alexei ; Andrews, Keith ; Schreck, Tobias |
| Type of entry: | Bibliographie |
| Title: | mVis in the Wild: Pre-Study of an Interactive Visual Machine Learning System for Labelling |
| Language: | English |
| Date: | 2019 |
| Publisher: | IEEE |
| Event Title: | IEEE VIS 2019 Workshop on Evaluation of Interactive Visual Machine Learning Systems (EVIVA-ML) |
| Event Location: | Vancouver, Canada |
| Event Dates: | 21.10. |
| Corresponding Links: | |
| 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. |
| Uncontrolled Keywords: | Human-centered computing, Visualization |
| Divisions: | 20 Department of Computer Science 20 Department of Computer Science > Interactive Graphics Systems |
| Date Deposited: | 27 Aug 2020 08:51 |
| Last Modified: | 27 Aug 2020 08:51 |
| PPN: | |
| Corresponding Links: | |
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