An authomatic method for construction of multi-classifier systems based on the combination of selection and fusion

• Ano de defesa: 2013
• Autor(a) principal: Lima, Tiago Pessoa Ferreira de
• Orientador(a): Ludermir, Teresa Bernarda
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Pernambuco
• Palavras-chave em Português: Sistemas de múltiplos classificadores; Comitês; Seleção e fusão; Mapas auto organizáveis; Perceptron de múltiplas camadas; Evolução diferencial adaptativa; Multi-classifier systems; Ensembles; Selection and fusion; Self-organizing maps; Multilayer perceptron; Adaptive differential evolution
• Link de acesso: https://repositorio.ufpe.br/handle/123456789/12457

In this dissertation, we present a methodology that aims the automatic construction of multi-classifiers systems based on the combination of selection and fusion. The presented method initially finds an optimum number of clusters for training data set and subsequently determines an ensemble for each cluster found. For model evaluation, the testing data set are submitted to clustering techniques and the nearest cluster to data input will emit a supervised response through its associated ensemble. Self-organizing maps were used in the clustering phase and multilayer perceptrons were used in the classification phase. Adaptive differential evolution has been used in this work in order to optimize the parameters and performance of the different techniques used in the classification and clustering phases. The proposed method, called SFJADE - Selection and Fusion (SF) via Adaptive Differential Evolution (JADE), has been tested on data compression of signals generated by artificial nose sensors and well-known classification problems, including cancer, card, diabetes, glass, heart, horse, soybean and thyroid. The experimental results have shown that the SFJADE method has a better performance than some literature methods while significantly outperforming most of the methods commonly used to construct Multi-Classifier Systems.