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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

Cited: 0

Clicked: 1341

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Mohammad Chegini

https://orcid.org/0000-0002-3516-8685

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

http://doi.org/10.1631/FITEE.1900549


Interactive visual labelling versus active learning: an experimental comparison


Author(s):  Mohammad Chegini, Jürgen 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, Jürgen 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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Abstract: 
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.

交互式可视化标注与主动学习:实验比较


Mohammad CHEGINI1,2,Jürgen BERNARD3,Jian CUI2,Fatemeh CHEGINI4,Alexei SOURIN2,Keith ANDREWS5,Tobias SCHRECK1
1格拉茨技术大学计算机图形与知识可视化研究所,奥地利格拉茨,8010
2南洋理工大学计算机科学与工程学院,新加坡,639798
3英属哥伦比亚大学信息可视化研究组,加拿大温哥华,V6T1Z4
4马克斯-普朗克气象研究所,德国汉堡,20146
5格拉茨技术大学互动系统与数据科学研究所,奥地利格拉茨,8010

摘要:监督式机器学习方法可自动分类新数据,且对数据分析非常有帮助。监督式机器学习的质量不仅依赖于使用的算法类型,也依赖于用于训练分类器的标注数据集的质量。训练数据集中的标注实例通常依赖于专业分析人员的手工选择与注释,且通常是一个单调与耗时的过程。标签可以在学习过程中为主动学习算法提供有用的输入,以自动确定数据实例的子集。交互式可视化标注技术是有前景的选择,它提供有效的视觉概览,分析人员可从中同时查看数据记录与选择项目标签。将分析人员置于循环中,生成的分类器可得到更高准确率。虽然交互式可视化标注技术的初步结果在某种意义上有前景的,考虑到用户标注可改善监督式学习,但是该技术的许多方面仍有待探索。本文使用mVis工具标注一个多元数据集以比较3种交互式可视化技术(相似图、散点矩阵与平行坐标图)以及主动学习。结果表明3种交互式可视化标注技术的分类准确率均高于主动学习算法,相对于散点矩阵与平行坐标图,用户主观上更偏爱使用相似图标注。用户也可以根据使用的可视化技术采用不同标注策略。

关键词:交互式可视化标注;主动学习;可视分析

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

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