mVis in the Wild: Pre-Study of an Interactive Visual Machine Learning System for Labelling

Mohammad Chegini, Jürgen Bernard, Lin Shao, Alexei Sourin, Keith Andrews, Tobias Schreck

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem Konferenzband

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.
Originalspracheenglisch
TitelProceeding of IEEE VIS 2019 Workshop on Evaluation of Interactive Visual Machine Learning Systems
Seitenumfang4
PublikationsstatusAngenommen/In Druck - 21 Okt 2019
VeranstaltungIEEE VIS 2019 Workshop on Evaluation of Interactive Visual Machine Learning Systems - VANCOUVER, BC, CANADA, Vancouver, Kanada
Dauer: 21 Okt 201921 Okt 2019
https://eviva-ml.github.io/

Workshop

WorkshopIEEE VIS 2019 Workshop on Evaluation of Interactive Visual Machine Learning Systems
KurztitelEVIVA-ML
LandKanada
OrtVancouver
Zeitraum21/10/1921/10/19
Internetadresse

Fingerprint Untersuchen Sie die Forschungsthemen von „mVis in the Wild: Pre-Study of an Interactive Visual Machine Learning System for Labelling“. Zusammen bilden sie einen einzigartigen Fingerprint.

Dieses zitieren