An investigation into the effects of label noise on dynamic selection algorithms

• Ano de defesa: 2020
• Autor(a) principal: WALMSLEY, Felipe Nunes
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso embargado
• Idioma: por
• Instituição de defesa: Universidade Federal de Pernambuco; UFPE; Brasil; Programa de Pos Graduacao em Ciencia da Computacao
• Palavras-chave em Português: Métodos de Ensemble; Sistemas de múltiplos classificadores; Seleção dinâmica; Ruído de classe
• Link de acesso: https://repositorio.ufpe.br/handle/123456789/37647

In the literature on classification problems, it is widely discussed how the presence of label noise can bring about severe degradation in performance. Several works have applied Prototype Selection techniques, Ensemble Methods, or both, in an attempt to alleviate this issue. Nevertheless, these methods are not always able to sufficiently counteract the effects of noise. In this work, we investigate the effects of noise on a particular class of Ensemble Methods, that of Dynamic Selection algorithms, and we are especially interested in the behavior of the Fire-DES++ algorithm, a state of the art algorithm which applies the ENN to algorithm to deal with the effects of noise and imbalance. We propose a method which employs multiple Dynamic Selection sets, based on the Bagging-IH algorithm, which we dub Multiple-Set Dynamic Selection (MSDS), in an attempt to supplant the ENN algorithm on the filtering step. We find that almost all methods based on Dynamic Selection are severely affected by the presence of label noise, with the exception of the KNORAU algorithm. We also find that our proposed method can alleviate the issues caused by noise in some specific scenarios.