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Detecting changes in data streams is an important area of research in many applications. The challenging issue is to know how to monitor, update and diagnose these changes so that the accuracy of the learner will be improved whatever the nature of the encountered drifts. In this paper a new error distance based approach for drift detection and monitoring, namely EDIST, is proposed. In EDIST, a difference in error distance distributions of two data-windows is monitored through a statistical hypothesis test. The proposed approach is tested using synthetic data and well known real world data sets. Encouraging results were found comparing to others similar approaches. EDIST has reached the best accuracies in most cases and shown more robustness to noise and false alarms.

Detecting changes in data streams attracts major attention in cognitive computing systems. The challenging issue is how to monitor and detect these changes in order to preserve the model performance during complex drifts. By complex drift, we mean a drift that presents many characteristics in the sometime. The most challenging complex drifts are gradual continuous drifts, where changes are only noticed during a long time period. Moreover, these gradual drifts may also be local, in the sense that they may affect a little amount of data, and thus make the drift detection more complicated. For this purpose, a new drift detection mechanism, EDIST2, is proposed in order to deal with these complex drifts. EDIST2 monitors the learner performance through a self-adaptive window that is autonomously adjusted through a statistical hypothesis test. This statistical test provides theoretical guarantees, regarding the false alarm rate, which were experimentally confirmed. EDIST2 has been tested through six synthetic datasets presenting different kinds of complex drift, and five real-world datasets. Encouraging results were found, comparing to similar approaches, where EDIST2 has achieved good accuracy rate in synthetic and realworld datasets and has achieved minimum delay of detection and false alarm rate.