A survey of visual analytics techniques for machine learning
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A survey of visual analytics techniques for machine learning Jun Yuan1 , Changjian Chen1 , Weikai Yang1 , Mengchen Liu2 , Jiazhi Xia3 , and Shixia Liu1 ( ) c The Author(s) 2020.
and machine learning techniques to facilitate the analysis and understanding of the major components in the learning process, with an aim to improve performance. For example, visual analytics research for explaining the inner workings of deep convolutional neural networks has increased the transparency of deep learning models and has received ongoing and increasing attention recently [1–4]. The rapid development of visual analytics techniques for machine learning yields an emerging need for a comprehensive review of this area to support the understanding of how visualization techniques are designed and applied to machine learning pipelines. There have been several initial efforts to summarize the advances in this field from different viewpoints. For example, Liu et al. [5] summarized visualization techniques for text analysis. Lu et al. [6] surveyed visual analytics techniques for predictive models. Recently, Liu et al. [1] presented a paper on the analysis of machine learning models from the visual analytics viewpoint. Sacha et al. [7] analyzed a set of example systems and proposed an ontology for visual analytics assisted machine learning. However, existing surveys either focus on a specific area of machine learning (e.g., text mining [5], predictive models [6], model understanding [1]) or aim to sketch an ontology [7] based on a set of example techniques only. In this paper, we aim to provide a comprehensive survey of visual analytics techniques for machine learning, which focuses on every phase of the machine learning pipeline. We focus on works in the visualization community. Nevertheless, the AI community has also made solid contributions to the study of visually explaining feature detectors in deep learning models. For example, Selvaraju et al. [8] tried to identify the part of an image to which its classification result is sensitive, by computing class
Abstract 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 modeling 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. Keywords visual analytics; machine learning; data quality; feature selection; model understanding; content analysis
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Introduction
The recent success of artificial intell
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