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.
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