Heitor Murilo Gomes

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This work encompasses the development of a new ensemble classifier that uses a Social Network abstraction for Data Stream Classification, namely the Social Adaptive Ensemble (SAE). In the context of data stream classification, concept drift is considered one of the most difficult and important issues to be addressed. Ensemble classifiers can be successfully(More)
Mining data streams is one of the main studies in machine learning area due to its application in many knowledge areas. One of the major challenges on mining data streams is concept drift, which requires the learner to discard the current concept and adapt to a new one. Ensemble-based drift detection algorithms have been used successfully to the(More)
This work presents two different voting strategies for ensemble learning on data streams based on pairwise combination of component classifiers. Despite efforts to build a diverse ensemble, there is always some degree of overlap between component classifiers models. Our voting strategies are aimed at using these overlaps to support ensemble prediction. We(More)
This work presents SAE2, a dynamic ensemble classifier for data stream classification that is built on the Social Adaptive Ensemble (SAE). Similarly to its predecessor, SAE2 maintains an ensemble of classifiers arranged as a network in which connections are created between two classifiers if they have similar predictions. In comparison with SAE, SAE2(More)
In this paper, we present a new ensemble method, the Scale-free Network Classifier (SFNClassifier), that is conceived as a dynamic sized scale-free network. In Data Stream Mining, ensemble-based approaches have been proposed to enhance accuracy and allow fast recovery from concept drift. However, these approaches are based on both update and polling(More)
Data stream classification has grown in importance in recent years due to the large amount of data rapidly generated in a multitude of domains. Many algorithms were proposed to deal with the problems associated with data stream classification (e.g. concept drifts) with special attention to ensemble methods. Ensembles are often preferred due to their(More)