AdaVis:适应性和可解释性可视化建议表格数据的表格数据
IEEE transactions on visualization and computer graphics
|September 18, 2023
概括
AdaVis提供一种可适应和可解释的机器学习 (ML) 方法来推可视化. 它解决了当前ML方法的局限性,通过建模一对多的数据到可视化关系,并提供特征重要性见解.
科学领域:
- 计算机科学 计算机科学
- 数据可视化 数据可视化
- 机器学习 机器学习
背景情况:
- 自动化可视化推帮助具有有限数据可视化专业知识和时间的用户.
- 当前的机器学习 (ML) 方法通常假设单个最佳可视化,这并不总是准确的.
- 现有的ML方法可能是不透明的,在他们的推推理中缺乏透明度.
研究的目的:
- 开发一种可适应和可解释的方法来推表格数据的可视化.
- 解决现有的基于ML的可视化推系统的局限性.
主要方法:
- 提出了AdaVis,这是一个可适应和可解释的方法,利用基于盒子嵌入的知识图.
- 模拟数据特征,数据集和可视化选择之间的一对多关系.
- 整合了一个注意力机制,用于特征的重要性和细粒度的解释性.
主要成果:
- 对于表式数据集,AdaVis有效地推一个或多个合适的可视化.
- 知识图成功地模拟了复杂的实体关系.
- 注意力机制为特征相关性提供了可解释的见解.
结论:
- 通过提供适应性和可解释性,AdaVis增强了可视化建议.
- 该方法通过处理多个有效可视化来改进现有的ML方法.
- 评估通过定量指标,案例研究和用户反证实了AdaVis的有效性.
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