Recurrent concepts in data streams classification
| Autoři | |
|---|---|
| Rok publikování | 2013 |
| Druh | Článek v odborném periodiku |
| Časopis / Zdroj | Knowledge and Information Systems |
| Fakulta / Pracoviště MU | |
| Citace | |
| www | http://dx.doi.org/10.1007/s10115-013-0654-6 |
| Doi | http://dx.doi.org/10.1007/s10115-013-0654-6 |
| Obor | Informatika |
| Klíčová slova | Data streams; Concept drift; Meta-learning; Recurrent concepts |
| Popis | 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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