Learning concept drift with ensembles of optimum-path forest-based classifiers

Concept drift methods learn patterns in non-stationary environments. Although such behavior is usually not expected in traditional classification problems, in real-world scenarios one can face them very much easier. In such a context, classifiers can be fooled and their effectiveness affected as well. Some examples include theft detection in energy distribution systems, where the consumer's behavior may change suddenly or smoothly, or even churn prediction in mobile companies. In this paper, we introduce the Optimum-Path Forest (OPF) classifier in the context of concept drift, using decisions for concept drift handling based on a committee of OPF classifiers. We consider three distinct perspectives (three rounds of experiments with variations of streaming managements) over publics datasets, being the results compared to the ones obtained by standard OPF. We consider OPF ensemble suitable to work under these dynamic scenarios since its recognition rates were considerably better when compared to traditional OPF. (C) 2019 Elsevier B.V. All rights reserved.