Multiple-Instance Learning through Optimum-Path Forest

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Afonso, Luis C. S.
Colombo, Danilo
Pereira, Clayton R. [UNESP]
Costa, Kelton A. P. [UNESP]
Papa, Joao P. [UNESP]

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Multiple-instance (MI) learning aims at modeling problems that are better described by several instances of a given sample instead of individual descriptions often employed by standard machine learning approaches. In binary-driven MI problems, the entire bag is considered positive if one (at least) sample is labeled as positive. On the other hand, a bag is considered negative if it contains all samples labeled as negative as well. In this paper, we introduced the Optimum-Path Forest (OPF) classifier to the context of multiple-instance learning paradigm, and we evaluated it in different scenarios that range from molecule description, text categorization, and anomaly detection in well-drilling report classification. The experimental results showed that two different OPF classifiers are very much suitable to handle problems in the multiple-instance learning paradigm.



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Proceedings of the International Joint Conference on Neural Networks, v. 2019-July.