A Multi-Class Probabilistic Optimum-Path Forest
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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.
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Multi-Class, Optimum-Path Forest, Probabilistic Classification
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Inglês
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Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, v. 5, p. 361-368.
