Recent Research Advances on Interactive Machine Learning
This survey paper provides a structured overview of IML research for researchers and practitioners, but it is incremental as it builds on existing work without introducing new methods or results.
The authors systematically reviewed recent literature on Interactive Machine Learning (IML) and developed a task-oriented taxonomy to classify it, concluding with a discussion of open challenges and research opportunities.
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.