Human-centered machine learning through interactive visualization

Sacha, Dominik, Sedlmair, Michael, Zhang, Leishi ORCID: https://orcid.org/0000-0002-3158-2328, Lee, John Aldo, Weiskopf, Daniel, North, Stephen and Keim, Daniel (2016) Human-centered machine learning through interactive visualization. ESANN 2016: 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning Bruges, Belgium April 27-28-29, 2016 Proceedings. In: 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 27-29 Apr 2016, Bruges, Belgium. ISBN 9782875870261. [Conference or Workshop Item]

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Abstract

The goal of visual analytics (VA) systems is to solve complex problems by integrating automated data analysis methods, such as machine learning (ML) algorithms, with interactive visualizations. We propose a conceptual framework that models human interactions with ML components in the VA process, and makes the crucial interplay between automated algorithms and interactive visualizations more concrete. The framework is illustrated through several examples. We derive three open research challenges at the intersection of ML and visualization research that will lead to more effective data analysis.

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