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Authors: Artur André A. M. Oliveira 1 ; Mateus Espadoto 1 ; Roberto Hirata Jr. 1 and Alexandru C. Telea 2

Affiliations: 1 Institute of Mathematics and Statistics, University of São Paulo, Brazil ; 2 Department of Information and Computing Sciences, Utrecht University, The Netherlands

Keyword(s): Machine Learning, Dimensionality Reduction, Dense Maps.

Abstract: Understanding the decision boundaries of a machine learning classifier is key to gain insight on how classifiers work. Recently, a technique called Decision Boundary Map (DBM) was developed to enable the visualization of such boundaries by leveraging direct and inverse projections. However, DBM have scalability issues for creating fine-grained maps, and can generate results that are hard to interpret when the classification problem has many classes. In this paper we propose a new technique called Supervised Decision Boundary Maps (SDBM), which uses a supervised, GPU-accelerated projection technique that solves the original DBM shortcomings. We show through several experiments that SDBM generates results that are much easier to interpret when compared to DBM, is faster and easier to use, while still being generic enough to be used with any type of single-output classifier.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Oliveira, A. ; Espadoto, M. ; Hirata Jr., R. and Telea, A. (2022). SDBM: Supervised Decision Boundary Maps for Machine Learning Classifiers. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - IVAPP; ISBN 978-989-758-555-5; ISSN 2184-4321, SciTePress, pages 77-87. DOI: 10.5220/0010896200003124

@conference{ivapp22,
author={Artur André A. M. Oliveira and Mateus Espadoto and Roberto {Hirata Jr.} and Alexandru C. Telea},
title={SDBM: Supervised Decision Boundary Maps for Machine Learning Classifiers},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - IVAPP},
year={2022},
pages={77-87},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010896200003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - IVAPP
TI - SDBM: Supervised Decision Boundary Maps for Machine Learning Classifiers
SN - 978-989-758-555-5
IS - 2184-4321
AU - Oliveira, A.
AU - Espadoto, M.
AU - Hirata Jr., R.
AU - Telea, A.
PY - 2022
SP - 77
EP - 87
DO - 10.5220/0010896200003124
PB - SciTePress

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