Recent research advances on interactive machine learning

  • Liu Jiang
  • Shixia LiuEmail author
  • Changjian Chen
Regular Paper


Interactive machine learning (IML) is an iterative learning process that tightly couples a human with a machine learner, which is widely used by researchers and practitioners to effectively solve a wide variety of real-world application problems. Although recent years have witnessed the proliferation of IML in the field of visual analytics, most recent surveys either focus on a specific area of IML or aim to summarize a visualization field that is too generic for IML. In this paper, we systematically review the recent literature on IML and classify them into a task-oriented taxonomy built by us. We conclude the survey with a discussion of open challenges and research opportunities that we believe are inspiring for future work in IML.

Graphical abstract


Interactive visualization Machine learning Interactive machine learning 


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Copyright information

© The Visualization Society of Japan 2018

Authors and Affiliations

  1. 1.National Engineering Lab for Big Data Software, and School of SoftwareTsinghua UniversityBeijingChina

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