Produção Nacional
Revisado por pares
Handling imbalanced datasets through Optimum-Path Forest
2022; Elsevier BV; Volume: 242; Linguagem: Inglês
10.1016/j.knosys.2022.108445
ISSN1872-7409
AutoresLeandro A. Passos, Danilo Samuel Jodas, Luiz Carlos Felix Ribeiro, Marco Akio, André Nunes de Souza, João Paulo Papa,
Tópico(s)Machine Learning and Data Classification
ResumoIn 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 $\text{O}^2$PF 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.
Referência(s)