Ensemble Classifiers for Drift Detection and Monitoring in Dynamical Environments

Imen Khamassi, Moamar Sayed-Mouchaweh, Moez Hammami, and Khaled Ghédira
Submission Type: 
Full Paper
phmc_13_078.pdf616.39 KBOctober 10, 2013 - 12:47am

Detecting and monitoring changes during the learning process are important areas of research in many industrial applications. The challenging issue is how to diagnose and analyze these changes so that the accuracy of the learning model can be preserved. Recently, ensemble classifiers have achieved good results when dealing with concept drifts. This paper presents two ensembles learning algorithms BagEDIST and BoostEDIST, which respectively combine the Online Bagging and the Online Boosting with the drift detection method EDIST. EDIST is a new drift detection method which monitors the distance between two consecutive errors of classification. The idea behind this combination is to develop an ensemble learning algorithm which explicitly handles concept drifts by providing useful descriptions about location, speed and severity of drifts. Moreover, this paper presents a new drift diversity measure in order to study the diversity of base classifiers and see how they cope with concept drifts. From various experiments, this new measure has provided a clearer vision about the ensemble’s behavior when dealing with concept drifts.

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Submission Keywords: 
Drift detection and monitoring
Non-stationary environments
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
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