Afonso, Luis C. S.Colombo, DaniloPereira, Clayton R. [UNESP]Costa, Kelton A. P. [UNESP]Papa, Joao P. [UNESP]IEEE2020-12-102020-12-102019-01-012019 International Joint Conference On Neural Networks (ijcnn). New York: Ieee, 7 p., 2019.2161-4393http://hdl.handle.net/11449/196859Multiple-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.7engTrabalho apresentado em eventoWOS:000530893806001