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Open AccessArticle

Constructing and Visualizing High-Quality Classifier Decision Boundary Maps

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Institute of Mathematics and Statistics, University of São Paulo, São Paulo 05508-090, Brazil
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Johann Bernoulli Institute, University of Groningen, 9747 AG Groningen, The Netherlands
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Department of Information and Computing Sciences, Utrecht University, 3584 CS Utrecht, The Netherlands
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Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in Information Visualization Theory and Applications (IVAPP 2019).
Information 2019, 10(9), 280; https://doi.org/10.3390/info10090280
Received: 15 July 2019 / Revised: 5 September 2019 / Accepted: 6 September 2019 / Published: 9 September 2019
(This article belongs to the Special Issue Information Visualization Theory and Applications (IVAPP 2019))
Visualizing decision boundaries of machine learning classifiers can help in classifier design, testing and fine-tuning. Decision maps are visualization techniques that overcome the key sparsity-related limitation of scatterplots for this task. To increase the trustworthiness of decision map use, we perform an extensive evaluation considering the dimensionality-reduction (DR) projection techniques underlying decision map construction. We extend the visual accuracy of decision maps by proposing additional techniques to suppress errors caused by projection distortions. Additionally, we propose ways to estimate and visually encode the distance-to-decision-boundary in decision maps, thereby enriching the conveyed information. We demonstrate our improvements and the insights that decision maps convey on several real-world datasets. View Full-Text
Keywords: machine learning; dimensionality reduction; image-based visualization machine learning; dimensionality reduction; image-based visualization
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Rodrigues, F.C.M.; Espadoto, M.; Hirata, R., Jr.; Telea, A.C. Constructing and Visualizing High-Quality Classifier Decision Boundary Maps. Information 2019, 10, 280.

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