What you see is what you can change: Human-centered machine learning by interactive visualization
What you see is what you can change: Human-centered machine learning by interactive visualization
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DOI：
10.1016/j.neucom.2017.01.105
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发表时间：
2017-12-13
期刊：
影响因子：
6
通讯作者：
Keim, Daniel A.
中科院分区：
文献类型：
--
作者：
Sacha, Dominik;Sedlmair, Michael;Keim, Daniel A.
可视化分析 (VA) 系统通过将自动化数据分析和挖掘（例如基于机器学习 (ML) 的方法）与交互式可视化相集成，帮助数据分析师以交互方式解决复杂问题。我们提出了一个概念框架，用于模拟 VA 过程中人类与 ML 组件的交互，并将自动化算法和交互式可视化之间的中心关系置于清晰的焦点。该框架通过几个示例进行了说明，我们通过识别 ML 方法通过交互式可视化与人类反馈相结合的关键场景，进一步详细阐述了交互式 ML 过程。我们在机器学习和可视化研究的交叉领域得出了五个开放研究挑战，其解决方案应该带来更有效的数据分析。 (C) 2017 Elsevier B.V. 保留所有权利。
Visual analytics (VA) systems help data analysts solve complex problems interactively, by integrating automated data analysis and mining, such as machine learning (ML) based methods, with interactive visualizations. We propose a conceptual framework that models human interactions with ML components in the VA process, and that puts the central relationship between automated algorithms and interactive visualizations into sharp focus. The framework is illustrated with several examples and we further elaborate on the interactive ML process by identifying key scenarios where ML methods are combined with human feedback through interactive visualization. We derive five open research challenges at the intersection of ML and visualization research, whose solution should lead to more effective data analysis. (C) 2017 Elsevier B.V. All rights reserved.