A survey of visual analytics techniques for machine learning

Computational Visual Media

Weikai Yang¹  Shixia Liu¹  Jun Yuan¹  Changjian Chen¹  Jiazhi Xia²  Mengchen Liu³ 

[1] BNRist, Tsinghua University, 100086, Beijing, China;Central South University, 410083, Changsha, China;Microsoft, 98052, Redmond, USA;

关键词: visual analytics;    machine learning;    data quality;    feature selection;    model understanding;    content analysis;   

DOI  :  10.1007/s41095-020-0191-7

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY