Journal
KNOWLEDGE AND INFORMATION SYSTEMS
Volume 40, Issue 3, Pages 489-507Publisher
SPRINGER LONDON LTD
DOI: 10.1007/s10115-013-0654-6
Keywords
Data streams; Concept drift; Meta-learning; Recurrent concepts
Funding
- ERDF-European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness)
- Portuguese Funds through the FCT (Portuguese Foundation for Science and Technology) [FCOMP-01-0124-FEDER-022701]
- project Knowledge Discovery from Ubiquitous Data Streams - FCT [PTDC/EIA/098355/2008]
- Masaryk University, Faculty of Informatics
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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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