A Multi-Class Probabilistic Optimum-Path Forest

Silas E. Nachif Fernandes, Leandro Passos, Danilo Jodas, Marco Akio, André Souza, João Papa

2023

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 context 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.

Paper Citation

in Harvard Style

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, SciTePress, pages 361-368. DOI: 10.5220/0011597700003417

in Bibtex Style

@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},
}

in EndNote Style

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
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