A Survey of Visual Analytics Techniques for Machine Learning
This is an incremental survey that provides a structured overview for researchers in visual analytics and machine learning to identify promising topics and apply relevant techniques.
The paper systematically reviews 259 papers from the last decade to survey visual analytics techniques for machine learning, building a taxonomy with three categories based on the model-building timeline and highlighting research challenges and future opportunities.
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 model 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.