A Survey of Visual Analytics Techniques for Machine Learning

Jun Yuan1   Changjian Chen1   Weikai Yang1   Mengchen Liu2   Jiazhi Xia3   Shixia Liu1

1Tsinghua University       2Microsoft       3Central South University

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An overview of visual analytics research for machine learning.


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 learn how to apply relevant techniques in visual analytics applications, we systematically review 259 papers that are published in the recent ten years or the representative works before 2010. We build a taxonomy, which includes three first-level categories: techniques before model building, techniques in 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 potential future research opportunities that can be promising and useful for visual analytics researchers.

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