Recent research advances on interactive machine learning

• Published in: Journal of visualization Vol. 22; no. 2; pp. 401 - 417
• Main Authors: Jiang, Liu, Liu, Shixia, Chen, Changjian
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 09.04.2019; Springer Nature B.V
• Subjects: Analytics; Artificial intelligence; Classical and Continuum Physics; Computer Imaging; Engineering; Engineering Fluid Dynamics; Engineering Thermodynamics; Heat and Mass Transfer; Literature reviews; Machine learning; Pattern Recognition and Graphics; Regular Paper; Taxonomy; Vision; Visual fields; Interactive visualization; Interactive machine learning; Machine learning
• ISSN: 1343-8875; 1875-8975
• DOI: 10.1007/s12650-018-0531-1

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