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

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

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

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.

Original language	English
Title of host publication	Proceeding of IEEE VIS 2019 Workshop on Evaluation of Interactive Visual Machine Learning Systems
Number of pages	4
Publication status	Accepted/In press - 21 Oct 2019
Event	IEEE VIS 2019 Workshop on Evaluation of Interactive Visual Machine Learning Systems - VANCOUVER, BC, CANADA, Vancouver, Canada Duration: 21 Oct 2019 → 21 Oct 2019 https://eviva-ml.github.io/

Workshop

Workshop	IEEE VIS 2019 Workshop on Evaluation of Interactive Visual Machine Learning Systems
Abbreviated title	EVIVA-ML
Country/Territory	Canada
City	Vancouver
Period	21/10/19 → 21/10/19
Internet address	https://eviva-ml.github.io/

Keywords

Visualisation
Machine Learning

Access to Document

mvis_evaluation_EVIVA_ML_2019Accepted author manuscript, 1.21 MB

Cite this

Chegini, M, Bernard, J, Shao, L, Sourin, A, Andrews, K & Schreck, T 2019, mVis in the Wild: Pre-Study of an Interactive Visual Machine Learning System for Labelling. in Proceeding of IEEE VIS 2019 Workshop on Evaluation of Interactive Visual Machine Learning Systems. IEEE VIS 2019 Workshop on Evaluation of Interactive Visual Machine Learning Systems, Vancouver, British Columbia, Canada, 21/10/19.

@inproceedings{540b4a2346354c6d905c9b9fa71a9b51,

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

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.",

keywords = "Visualisation, Machine Learning",

author = "Mohammad Chegini and J{\"u}rgen Bernard and Lin Shao and Alexei Sourin and Keith Andrews and Tobias Schreck",

year = "2019",

month = oct,

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language = "English",

booktitle = "Proceeding of IEEE VIS 2019 Workshop on Evaluation of Interactive Visual Machine Learning Systems",

note = "IEEE VIS 2019 Workshop on Evaluation of Interactive Visual Machine Learning Systems, EVIVA-ML ; Conference date: 21-10-2019 Through 21-10-2019",

url = "https://eviva-ml.github.io/",

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T1 - mVis in the Wild: Pre-Study of an Interactive Visual Machine Learning System for Labelling

AU - Chegini, Mohammad

AU - Bernard, Jürgen

AU - Shao, Lin

AU - Sourin, Alexei

AU - Andrews, Keith

AU - Schreck, Tobias

PY - 2019/10/21

Y1 - 2019/10/21

N2 - 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.

AB - 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.

KW - Visualisation

KW - Machine Learning

M3 - Conference paper

BT - Proceeding of IEEE VIS 2019 Workshop on Evaluation of Interactive Visual Machine Learning Systems

T2 - IEEE VIS 2019 Workshop on Evaluation of Interactive Visual Machine Learning Systems

Y2 - 21 October 2019 through 21 October 2019

ER -

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

Abstract

Workshop

Keywords

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