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Authors: Silas E. Nachif Fernandes 1 ; 3 ; Leandro Passos 2 ; Danilo Jodas 1 ; André Souza 3 and João Papa 1

Affiliations: 1 Department of Computing, São Paulo State University, Bauru, Brazil ; 2 School of Engineering and Informatics, University Wolverhampton, Wolverhampton, England, U.K. ; 3 Department of Electrical Engineering, São Paulo State University, Bauru, Brazil

Keyword(s): Optimum-Path Forest, Probabilistic Classification, Multi-Class.

Abstract: The advent of machine learning provided numerous benefits to humankind, impacting fields such as medicine, military, and entertainment, to cite a few. In most cases, given some instances from a previously known domain, the intelligent algorithm is encharged of predicting a label that categorizes such samples in some learned context. Among several techniques capable of accomplishing such classification tasks, one may refer to Support Vector Machines, Neural Networks, or graph-based classifiers, such as the Optimum-Path Forest (OPF). Even though such a paradigm satisfies a wide sort of problems, others require the predicted class label and the classifier’s confidence, i.e., how sure the model is while attributing labels. Recently, an OPF-based variant was proposed to tackle this problem, i.e., the Probabilistic Optimum-Path Forest. Despite its satisfactory results over a considerable number of datasets, it was conceived to deal with binary classification only, thus lacking in the conte xt of multi-class problems. Therefore, this paper proposes the Multi-Class Probabilistic Optimum-Path Forest, an extension designed to outdraw limitations observed in the standard Probabilistic OPF. (More)

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Paper citation in several formats:
E. Nachif Fernandes, S.; Passos, L.; Jodas, D.; Akio, M.; Souza, A. and Papa, J. (2023). A Multi-Class Probabilistic Optimum-Path Forest. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP; ISBN 978-989-758-634-7; ISSN 2184-4321, SciTePress, pages 361-368. DOI: 10.5220/0011597700003417

@conference{visapp23,
author={Silas {E. Nachif Fernandes}. and Leandro Passos. and Danilo Jodas. and Marco Akio. and André Souza. and João Papa.},
title={A Multi-Class Probabilistic Optimum-Path Forest},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP},
year={2023},
pages={361-368},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011597700003417},
isbn={978-989-758-634-7},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP
TI - A Multi-Class Probabilistic Optimum-Path Forest
SN - 978-989-758-634-7
IS - 2184-4321
AU - E. Nachif Fernandes, S.
AU - Passos, L.
AU - Jodas, D.
AU - Akio, M.
AU - Souza, A.
AU - Papa, J.
PY - 2023
SP - 361
EP - 368
DO - 10.5220/0011597700003417
PB - SciTePress