Recurrent concepts in data streams classification
| Authors | |
|---|---|
| Year of publication | 2013 |
| Type | Article in Periodical |
| Magazine / Source | Knowledge and Information Systems |
| MU Faculty or unit | |
| Citation | |
| Web | http://dx.doi.org/10.1007/s10115-013-0654-6 |
| Doi | http://dx.doi.org/10.1007/s10115-013-0654-6 |
| 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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