Publication details

Recurrent concepts in data streams classification



Year of publication 2013
Type Article in Periodical
Magazine / Source Knowledge and Information Systems
MU Faculty or unit

Faculty of Informatics

Field Informatics
Keywords Data streams; Concept drift; Meta-learning; Recurrent concepts
Description This work addresses the problem of mining data streams generated in dynamic environments where the distribution underlying the observations may change over time. We present a system that monitors the evolution of the learning process. The system is able to self-diagnose degradations of this process, using change detection mechanisms, and self-repair the decision models. The system uses meta-learning techniques that characterize the domain of applicability of previously learned models. The meta-learner can detect recurrence of contexts, using unlabeled examples, and take pro-active actions by activating previously learned models. The experimental evaluation on three text mining problems demonstrates the main advantages of the proposed system: it provides information about the recurrence of concepts and rapidly adapts decision models when drift occurs.
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