Tiago P. F. de Lima

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This paper explores the automatic construction of multiple classifiers systems using the selection method. The automatic method proposed is composed by two phases: one for designing the individual classifiers and one for clustering patterns of training set and search specialized classifiers for each cluster found. The performed experiments adopted the(More)
Diversity is deemed to be a key issue in classifier combination. For this reason, not every classifier is an expert for every query pattern. Thus, many researchers have focused on dynamic ensemble selection. Most works, however, use only one criterion to perform the dynamic selection. Hence, multiple criteria can provide a decision more effective than the(More)
Ensemble of classifier is an effective way of improving performance of individual classifiers. However, the choice of the ensemble members can become a very difficult task, which, in some cases, can lead to ensembles with no performance improvement. Dynamic ensemble selection systems aim to select a group of classifiers that is most adequate for a specific(More)
We present a methodology for the automatic construction of multi-classifiers systems based on the combination of selection and fusion. The proposed methodology initially finds the optimum number of clusters for training data set and subsequently determines an ensemble for each cluster found. Self-organizing maps were used in the clustering phase, and(More)
This paper evaluates some strategies to approximate the performance of dynamic ensembles based on NN-rule to the oracle performance. For this purpose, we use a multi-objective optimization algorithm, based on Differential Evolution, to generate automatically a pool of accurate and diverse classifiers in the form of Extreme Learning Machines. However, the(More)
Extreme Learning Machine (ELM) is a single-hidden-layer feedforward neural network which has been applied into many real world pattern classification problems. Recently, ELMs have been built in an automatic way through evolutionary algorithms. Most works, nonetheless, do not uses all population obtained, but choose only one individual in the last(More)
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