loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Mateus Espadoto 1 ; Francisco Caio M. Rodrigues 1 and Alexandru C. Telea 2

Affiliations: 1 Institute of Mathematics and Statistics, University of São Paulo, Brazil, Johann Bernoulli Institute, University of Groningen and The Netherlands ; 2 Johann Bernoulli Institute, University of Groningen and The Netherlands

Keyword(s): Machine Learning, Dimensionality Reduction, Image-based Visualization.

Related Ontology Subjects/Areas/Topics: Abstract Data Visualization ; Computer Vision, Visualization and Computer Graphics ; General Data Visualization ; High-Dimensional Data and Dimensionality Reduction ; Information and Scientific Visualization ; Visual Data Analysis and Knowledge Discovery

Abstract: Visualizing decision boundaries of modern machine learning classifiers can notably help in classifier design, testing, and fine-tuning. Dense maps are a very recent method that overcomes the key sparsity-related limitation of scatterplots for this task. However, the trustworthiness of dense maps heavily depends on the underlying dimensionality-reduction (DR) techniques they use. We design and perform a detailed study aimed at finding the best DR techniques to use when creating trustworthy dense maps, by studying a large collection of 28 DR algorithms, 4 classifiers, and 2 datasets from a real-world challenging classification problem. Our results show how one can pick suitable DR algorithms to create dense maps that help understanding classifier behavior.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.212.102.174

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Espadoto, M.; Rodrigues, F. and Telea, A. (2019). Visual Analytics of Multidimensional Projections for Constructing Classifier Decision Boundary Maps. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - IVAPP; ISBN 978-989-758-354-4; ISSN 2184-4321, SciTePress, pages 28-38. DOI: 10.5220/0007260800280038

@conference{ivapp19,
author={Mateus Espadoto. and Francisco Caio M. Rodrigues. and Alexandru C. Telea.},
title={Visual Analytics of Multidimensional Projections for Constructing Classifier Decision Boundary Maps},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - IVAPP},
year={2019},
pages={28-38},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007260800280038},
isbn={978-989-758-354-4},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - IVAPP
TI - Visual Analytics of Multidimensional Projections for Constructing Classifier Decision Boundary Maps
SN - 978-989-758-354-4
IS - 2184-4321
AU - Espadoto, M.
AU - Rodrigues, F.
AU - Telea, A.
PY - 2019
SP - 28
EP - 38
DO - 10.5220/0007260800280038
PB - SciTePress