Dilema da diversidade-acurácia: um estudo empírico no contexto de multiclassificadores

AUTOR(ES)
DATA DE PUBLICAÇÃO

2008

RESUMO

Multi-classifier systems, also known as ensembles, have been widely used to solve several problems, because they, often, present better performance than the individual classifiers that form these systems. But, in order to do so, its necessary that the base classifiers to be as accurate as diverse among themselves this is also known as diversity/accuracy dilemma. Given its importance, some works have investigate the ensembles behavior in context of this dilemma. However, the majority of them address homogenous ensemble, i.e., ensembles composed only of the same type of classifiers. Thus, motivated by this limitation, this thesis, using genetic algorithms, performs a detailed study on the dilemma diversity/accuracy for heterogeneous ensembles

ASSUNTO(S)

algoritmo genético multiobjetivo sistemas de computacao comitês de classificadores multi-objective genetic algorithm, classifier combination systems ensembles sistemas de combinação de classificadores

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