Revisitando o problema de classificação de padrões na presença de outliers usando técnicas de regressão robusta

Detalhes bibliográficos
Ano de defesa: 2013
Autor(a) principal: Barros, Ana Luiza Bessa de Paula
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Tese
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Não Informado pela instituição
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: http://www.repositorio.ufc.br/handle/riufc/8003
Resumo: This thesis addresses the problem of data classification when they are contaminated with atypical patterns. These patterns, generally called outliers, are omnipresent in real-world multi- variate data sets, but their a priori detection (i.e. before training the classifier) is a difficult task to perform. As a result, the most common approach is the reactive one, in which one suspects of the presence of outliers in the data only after a previously trained classifier has achieved a low performance. Several strategies can then be carried out to improve the performance of the classifier, such as to choose a more computationally powerful classifier and/or to remove the de- tected outliers from data, eliminating those patterns which are difficult to categorize properly. Whatever the strategy adopted, the presence of outliers will always require more attention and care during the design of a pattern classifier. Bearing these difficulties in mind, this thesis revi- sits concepts and techniques from the theory of robust regression, in particular those related to M-estimation, adapting them to the design of pattern classifiers which are able to automatically handle outliers. This adaptation leads to the proposal of robust versions of two pattern classi- fiers widely used in the literature, namely, least squares classifier (LSC) and extreme learning machine (ELM). Through a comprehensive set of computer experiments using synthetic and real-world data, it is shown that the proposed robust classifiers consistently outperform their original versions.