Serviços Personalizados
Journal
Artigo
Indicadores
Citado por SciELO
Acessos
Links relacionados
Similares em SciELO
Compartilhar
Computación y Sistemas
versão On-line ISSN 2007-9737versão impressa ISSN 1405-5546
Resumo
GUERRA, Mayte e MADERA, Julio. WIFROWAN: Wrapped Imbalanced Fuzzy-Rough Ordered Weighted Average Nearest Neighbor Classification. Comp. y Sist. [online]. 2020, vol.24, n.3, pp.957-968. Epub 09-Jun-2021. ISSN 2007-9737. https://doi.org/10.13053/cys-24-3-3054.
In this paper we propose an ensemble method based on IFROWANN (Imbalanced Fuzzy-Rough Ordered Weighted Average Nearest Neighbor) algorithm to classify problems with imbalanced data. The ensemble generates many classifiers with different weight strategy and indiscernibility fuzzy relations. Classification is carried out selecting one of three strategies: I- to classify the new instance with the algorithm with best AUC in training. II- to average the memberships of the instance to the fuzzy-rough lower and upper approximation of each class given by the classifiers with best AUC. III- to average the memberships of the instance to the fuzzy-rough lower and upper approximation of each class of the all classifiers. Our method is validated by an extensive experimental study, showing statistically better results than 14 other state-of-the-art methods.
Palavras-chave : Ensemble; imbalanced classification; fuzzy-rough sets.
![](/img/en/iconPDFDocument.gif)