InstanceFlow: Visualizing the Evolution of Classifier Confusion on the Instance Level

InstanceFlow Teaser

Abstract

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 interpretabilty 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.

Citation

Michael PĆ¼hringer, Andreas Hinterreiter, Marc Streit
InstanceFlow: Visualizing the Evolution of Classifier Confusion on the Instance Level
Proceedings of the IEEE Visualization Short Papers, 2020.

BibTeX

@inproceedings{2020_preprint_instanceflow,
    title = {InstanceFlow: Visualizing the Evolution of Classifier Confusion on the Instance Level},
    author = {Michael PĆ¼hringer and Andreas Hinterreiter and Marc Streit},
    booktitle = {Proceedings of the IEEE Visualization Short Papers},
    publisher = {IEEE Computer Society Press},
    url = {https://arxiv.org/abs/2007.11353},
    year = {2020}
}

Acknowledgements

This work was supported in part by the State of Upper Austria and the Austrian Federal Ministry of Education, Science and Research via the Linz Institute of Technology (LIT-2019-7-SEE-117), by the State of Upper Austria (Human-Interpretable ML), and by the Austrian Research Promotion Agency (FFG~854184). Pro2Future is funded within the Austrian COMET Program (Competence Centers for Excellent Technologies) under the auspices of the Austrian Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology, the Austrian Federal Ministry for Digital and Economic Affairs and of the States of Upper Austria and Styria. COMET is managed by FFG.