Multiple-Instance Learning through Optimum-Path Forest

• Main Author: Afonso, Luis C. S.
• Publication Date: 2019
• Other Authors: Colombo, Danilo, Pereira, Clayton R. [UNESP], Costa, Kelton A. P. [UNESP], Papa, Joao P. [UNESP]
• Format: Conference object
• Language: eng
• Source: Repositório Institucional da UNESP
• Download full: http://dx.doi.org/10.1109/IJCNN.2019.8852454; http://hdl.handle.net/11449/201222

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.