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)
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)
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)
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)
Data Stream Clustering is an active area of research which requires efficient algorithms capable of finding and updating clusters incrementally. On top of that, due to the inherent evolving nature of data streams, it is expected that these algorithms manage to quickly adapt to both concept drifts and the appearance and disappearance of clusters.(More)