Recent Research Advances on Interactive Machine Learning

TL;DR

This paper provides a comprehensive review of recent advances in Interactive Machine Learning, categorizing key research and identifying open challenges to guide future developments in the field.

Contribution

It offers a systematic classification of recent IML research into a task-oriented taxonomy and discusses open challenges and future research opportunities.

Findings

IML is increasingly used in visual analytics applications.

A new taxonomy categorizes recent IML research by task.

Open challenges include scalability and user interaction design.

Abstract

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.