Handling imbalanced datasets through Optimum-Path Forest

• Autor(a) principal: Passos, Leandro Aparecido [UNESP]
• Data de Publicação: 2022
• Outros Autores: Jodas, Danilo S. [UNESP], Ribeiro, Luiz C.F. [UNESP], Akio, Marco [UNESP], de Souza, Andre Nunes [UNESP], Papa, João Paulo [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1016/j.knosys.2022.108445; http://hdl.handle.net/11449/234201
• topic: Imbalanced data; Optimum-Path Forest; Oversampling; Undersampling

In the last decade, machine learning-based approaches became capable of performing a wide range of complex tasks sometimes better than humans, demanding a fraction of the time. Such an advance is partially due to the exponential growth in the amount of data available, which makes it possible to extract trustworthy real-world information from them. However, such data is generally imbalanced since some phenomena are more likely than others. Such a behavior yields considerable influence on the machine learning model's performance since it becomes biased on the more frequent data it receives. Despite the considerable amount of machine learning methods, a graph-based approach has attracted considerable notoriety due to the outstanding performance over many applications, i.e., the Optimum-Path Forest (OPF). In this paper, we propose three OPF-based strategies to deal with the imbalance problem: the O2PF and the OPF-US, which are novel approaches for oversampling and undersampling, respectively, as well as a hybrid strategy combining both approaches. The paper also introduces a set of variants concerning the strategies mentioned above. Results compared against several state-of-the-art techniques over public and private datasets confirm the robustness of the proposed approaches.