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CLC number: TP311

On-line Access: 2020-04-21

Received: 2019-10-06

Revision Accepted: 2020-01-17

Crosschecked: 2020-01-30

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Citations:  Bibtex RefMan EndNote GB/T7714


Mohammad Chegini


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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.4 P.524-535


Interactive visual labelling versus active learning: an experimental comparison

Author(s):  Mohammad Chegini, Jrgen Bernard, Jian Cui, Fatemeh Chegini, Alexei Sourin, Keith Andrews, Tobias Schreck

Affiliation(s):  Institute of Computer Graphics and Knowledge Visualisation, Graz University of Technology, Graz 8010, Austria; more

Corresponding email(s):   m.chegini@cgv.tugraz.at, jubernar@cs.ubc.ca, assourin@ntu.edu.sg, kandrews@tugraz.at

Key Words:  Interactive visual labelling, Active learning, Visual analytics

Mohammad Chegini, Jrgen Bernard, Jian Cui, Fatemeh Chegini, Alexei Sourin, Keith Andrews, Tobias Schreck. Interactive visual labelling versus active learning: an experimental comparison[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(4): 524-535.

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Methods from supervised machine learning allow the classification of new data automatically and are tremendously helpful for data analysis. The quality of supervised maching learning depends not only on the type of algorithm used, but also on the quality of the labelled dataset used to train the classifier. Labelling instances in a training dataset is often done manually relying on selections and annotations by expert analysts, and is often a tedious and time-consuming process. active learning algorithms can automatically determine a subset of data instances for which labels would provide useful input to the learning process. interactive visual labelling techniques are a promising alternative, providing effective visual overviews from which an analyst can simultaneously explore data records and select items to a label. By putting the analyst in the loop, higher accuracy can be achieved in the resulting classifier. While initial results of interactive visual labelling techniques are promising in the sense that user labelling can improve supervised learning, many aspects of these techniques are still largely unexplored. This paper presents a study conducted using the mVis tool to compare three interactive visualisations, similarity map, scatterplot matrix (SPLOM), and parallel coordinates, with each other and with active learning for the purpose of labelling a multivariate dataset. The results show that all three interactive visual labelling techniques surpass active learning algorithms in terms of classifier accuracy, and that users subjectively prefer the similarity map over SPLOM and parallel coordinates for labelling. Users also employ different labelling strategies depending on the visualisation used.


Mohammad CHEGINI1,2,Jürgen BERNARD3,Jian CUI2,Fatemeh CHEGINI4,Alexei SOURIN2,Keith ANDREWS5,Tobias SCHRECK1



Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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