InstanceFlow：在实例级别上可视化分类器混淆的演变
InstanceFlow: Visualizing the Evolution of Classifier Confusion on the Instance Level
摘要：
分类是有监督机器学习中最重要的任务之一。在分类模型的训练期间，训练实例被多次（在多个时期期间）馈送到模型，以便迭代地提高分类性能。随着模型复杂性的增加，对可视化模型可解释性的需求也越来越大。现有的方法大多集中在训练后的最终模型性能的视觉分析，往往局限于聚合性能的措施。在本文中，我们介绍了InstanceFlow，一种新的双视图可视化工具，允许用户分析分类器的学习行为随着时间的推移在实例级。Sankey图可视化了整个时期的实例流，并为单个实例提供了按需的详细图示和跟踪。表格视图允许用户通过排名和过滤来定位感兴趣的实例。通过这种方式，InstanceFlow弥合了类级和实例级性能评估之间的差距，同时使用户能够对训练过程执行完整的时间分析。
英文摘要：
Classification is one of the most important supervised machine learning tasks. During the training of a classification model, the training instances are fed to the model multiple times (during multiple epochs) in order to iteratively increase the classification performance. The increasing complexity of models has led to a growing demand for model interpretability through visualizations. Existing approaches mostly focus on the visual analysis of the final model performance after training and are often limited to aggregate performance measures. In this paper we introduce InstanceFlow, a novel dual-view visualization tool that allows users to analyze the learning behavior of classifiers over time on the instance-level. A Sankey diagram visualizes the flow of instances throughout epochs, with on-demand detailed glyphs and traces for individual instances. A tabular view allows users to locate interesting instances by ranking and filtering. In this way, InstanceFlow bridges the gap between class-level and instance-level performance evaluation while enabling users to perform a full temporal analysis of the training process.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
