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Review Article | Open Access

A survey of visual analytics techniques for machine learning

BNRist, Tsinghua University, Beijing 100086, China
Microsoft, Redmond 98052, USA
Central South University, Changsha 410083, China
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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 analyticsmachine learningdata qualityfeature selectionmodel under-standingcontent analysis

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Computational Visual Media
Volume 7 Issue 1,
March 2021
Pages 3-36
DOI: 10.1007/s41095-020-0191-7

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Cite this article:
Yuan J, Chen C, Yang W, et al. A survey of visual analytics techniques for machine learning. Computational Visual Media, 2021, 7(1): 3-36. https://doi.org/10.1007/s41095-020-0191-7

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Received: 12 July 2020
Accepted: 04 August 2020
Published: 25 November 2020
© The Author(s) 2020

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