Abstract
| Sprache | englisch |
|---|---|
| Seiten | 9 - 17 |
| Seitenumfang | 9 |
| Fachzeitschrift | Visual Informatics |
| Jahrgang | 3 |
| Ausgabennummer | 1 |
| DOIs | |
| Status | Veröffentlicht - 1 Mär 2019 |
| Veranstaltung | PacificVAST 2019 - Bangkok, Bangkok, Thailand Dauer: 23 Apr 2019 → … http://research.cbs.chula.ac.th/pvis2019/PacificVAST.aspx |
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Interactive labelling of a multivariate dataset for supervised machine learning using linked visualisations, clustering, and active learning. / Chegini, Mohammad; Bernard, Jürgen; Berger, Philip; Sourin, Alexei; Andrews, Keith; Schreck, Tobias.
in: Visual Informatics, Jahrgang 3, Nr. 1, 01.03.2019, S. 9 - 17.Publikation: Beitrag in einer Fachzeitschrift › Artikel › Forschung › Begutachtung
}
TY - JOUR
T1 - Interactive labelling of a multivariate dataset for supervised machine learning using linked visualisations, clustering, and active learning
AU - Chegini, Mohammad
AU - Bernard, Jürgen
AU - Berger, Philip
AU - Sourin, Alexei
AU - Andrews, Keith
AU - Schreck, Tobias
N1 - SI: Proceedings of PacificVAST 2019
PY - 2019/3/1
Y1 - 2019/3/1
N2 - Supervised machine learning techniques require labelled multivariate training datasets. Many approaches address the issue of unlabelled datasets by tightly coupling machine learning algorithms with interactive visualisations. Using appropriate techniques, analysts can play an active role in a highly interactive and iterative machine learning process to label the dataset and create meaningful partitions. While this principle has been implemented either for unsupervised, semi-supervised, or supervised machine learning tasks, the combination of all three methodologies remains challenging. In this paper, a visual analytics approach is presented, combining a variety of machine learning capabilities with four linked visualisation views, all integrated within the mVis (multivariate Visualiser) system. The available palette of techniques allows an analyst to perform exploratory data analysis on a multivariate dataset and divide it into meaningful labelled partitions, from which a classifier can be built. In the workflow, the analyst can label interesting patterns or outliers in a semi-supervised process supported by active learning. Once a dataset has been interactively labelled, the analyst can continue the workflow with supervised machine learning to assess to what degree the subsequent classifier has effectively learned the concepts expressed in the labelled training dataset. Using a novel technique called automatic dimension selection, interactions the analyst had with dimensions of the multivariate dataset are used to steer the machine learning algorithms. A real-world football dataset is used to show the utility of mVis for a series of analysis and labelling tasks, from initial labelling through iterations of data exploration, clustering, classification, and active learning to refine the named partitions, to finally producing a high-quality labelled training dataset suitable for training a classifier. The tool empowers the analyst with interactive visualisations including scatterplots, parallel coordinates, similarity maps for records, and a new similarity map for partitions.
AB - Supervised machine learning techniques require labelled multivariate training datasets. Many approaches address the issue of unlabelled datasets by tightly coupling machine learning algorithms with interactive visualisations. Using appropriate techniques, analysts can play an active role in a highly interactive and iterative machine learning process to label the dataset and create meaningful partitions. While this principle has been implemented either for unsupervised, semi-supervised, or supervised machine learning tasks, the combination of all three methodologies remains challenging. In this paper, a visual analytics approach is presented, combining a variety of machine learning capabilities with four linked visualisation views, all integrated within the mVis (multivariate Visualiser) system. The available palette of techniques allows an analyst to perform exploratory data analysis on a multivariate dataset and divide it into meaningful labelled partitions, from which a classifier can be built. In the workflow, the analyst can label interesting patterns or outliers in a semi-supervised process supported by active learning. Once a dataset has been interactively labelled, the analyst can continue the workflow with supervised machine learning to assess to what degree the subsequent classifier has effectively learned the concepts expressed in the labelled training dataset. Using a novel technique called automatic dimension selection, interactions the analyst had with dimensions of the multivariate dataset are used to steer the machine learning algorithms. A real-world football dataset is used to show the utility of mVis for a series of analysis and labelling tasks, from initial labelling through iterations of data exploration, clustering, classification, and active learning to refine the named partitions, to finally producing a high-quality labelled training dataset suitable for training a classifier. The tool empowers the analyst with interactive visualisations including scatterplots, parallel coordinates, similarity maps for records, and a new similarity map for partitions.
KW - Labelling, Clustering, Classification, Active learning, Multivariate data, Visualisation
KW - Multivariate data
KW - Active learning
KW - Classification
KW - Visualisation
KW - Labelling
KW - Clustering
UR - http://www.scopus.com/inward/record.url?scp=85066328558&partnerID=8YFLogxK
U2 - 10.1016/j.visinf.2019.03.002
DO - 10.1016/j.visinf.2019.03.002
M3 - Article
VL - 3
SP - 9
EP - 17
JO - Visual Informatics
T2 - Visual Informatics
JF - Visual Informatics
SN - 2468-502X
IS - 1
ER -