Task-Based Visual Interactive Modeling: Decision Trees and Rule-Based Classifiers
Task-Based Visual Interactive Modeling: Decision Trees and Rule-Based Classifiers
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DOI：
10.1109/tvcg.2020.3045560
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发表时间：
2022-09-01
影响因子：
5.2
通讯作者：
Keim，Daniel A.
中科院分区：
文献类型：
--
作者：
Streeb，Dirk;Metz，Yannick;Keim，Daniel A.
可视化分析使机器学习模型和人类在紧密集成的工作流中耦合在一起，解决各种分析任务。每个任务都对分析师和决策者提出了不同的要求。在本研究中，我们重点研究了一种基于规则的分类技术，即决策树分类器。我们概述了决策树的可用可视化，重点介绍了16项任务的可视化差异。此外，我们还调查了所采用的视觉设计的类型，以及所提出的质量措施。我们发现(i)用于分类器开发的交互式视觉分析系统提供了各种视觉设计，（ii）利用任务很少覆盖，（iii）除了分类器开发之外，节点链接图无处不在，（iv）甚至为机器学习专家设计的系统也很少具有除准确性之外的质量度量的视觉表示。总之，我们看到了集成算法技术、数学质量度量和定制交互式可视化的潜力，使人类专家能够更有效地利用他们的知识。
Visual analytics enables the coupling of machine learning models and humans in a tightly integrated workflow, addressing various analysis tasks. Each task poses distinct demands to analysts and decision-makers. In this survey, we focus on one canonical technique for rule-based classification, namely decision tree classifiers. We provide an overview of available visualizations for decision trees with a focus on how visualizations differ with respect to 16 tasks. Further, we investigate the types of visual designs employed, and the quality measures presented. We find that (i) interactive visual analytics systems for classifier development offer a variety of visual designs, (ii) utilization tasks are sparsely covered, (iii) beyond classifier development, node-link diagrams are omnipresent, (iv) even systems designed for machine learning experts rarely feature visual representations of quality measures other than accuracy. In conclusion, we see a potential for integrating algorithmic techniques, mathematical quality measures, and tailored interactive visualizations to enable human experts to utilize their knowledge more effectively.