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Visual analytics for machine learning has recently evolved as one of the most exciting areas in the field of visualization. To better identify which research topics are promising and to learn how to apply relevant techniques in visual analytics, we systematically review 259 papers published in the last ten years together with representative works before 2010. We build a taxonomy, which includes three first-level categories: techniques before model building, techniques during modeling building, and techniques after model building. Each category is further characterized by representative analysis tasks, and each task is exemplified by a set of recent influential works. We also discuss and highlight research challenges and promising potential future research opportunities useful for visual analytics researchers.


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A survey of visual analytics techniques for machine learning

Show Author's information Jun Yuan1Changjian Chen1Weikai Yang1Mengchen Liu2Jiazhi Xia3Shixia Liu1( )
BNRist, Tsinghua University, Beijing 100086, China
Microsoft, Redmond 98052, USA
Central South University, Changsha 410083, China

Abstract

Visual analytics for machine learning has recently evolved as one of the most exciting areas in the field of visualization. To better identify which research topics are promising and to learn how to apply relevant techniques in visual analytics, we systematically review 259 papers published in the last ten years together with representative works before 2010. We build a taxonomy, which includes three first-level categories: techniques before model building, techniques during modeling building, and techniques after model building. Each category is further characterized by representative analysis tasks, and each task is exemplified by a set of recent influential works. We also discuss and highlight research challenges and promising potential future research opportunities useful for visual analytics researchers.

Keywords:

visual analytics, machine learning, data quality, feature selection, model under-standing, content analysis
Received: 12 July 2020 Accepted: 04 August 2020 Published: 25 November 2020 Issue date: March 2021
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Publication history

Received: 12 July 2020
Accepted: 04 August 2020
Published: 25 November 2020
Issue date: March 2021

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© The Author(s) 2020

Acknowledgements

This research is supported by the National Key R&D Program of China (Nos. 2018YFB1004300 and2019YFB1405703), the National Natural Science Foundation of China (Nos. 61761136020, 61672307,61672308, and 61936002), TC190A4DA/3, the InstituteGuo Qiang, Tsinghua University, and in part by Tsinghua-Kuaishou Institute of Future Media Data.

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