Tracking Recurring Concepts with Meta-learners

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F09%3A00037443" target="_blank" >RIV/00216224:14330/09:00037443 - isvavai.cz</a>
Alternative codes found
RIV/00216224:14330/09:00067155
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Tracking Recurring Concepts with Meta-learners
Original language description
This work address data stream mining from dynamic environments where the distribution underlying the observations may change over time. In these contexts, learning algorithms must be equipped with change detection mechanisms. Several methods have been proposed able to detect and react to concept drift. When a drift is signaled, most of the approaches use a forgetting mechanism, by releasing the current model, and start learning a new decision model. It is not rare for the concepts from history to reappear, for example seasonal changes. In this work we present method that memorizes learnt models and uses meta-learning techniques that characterize the domain of applicability of previous models. The meta-learner can detect re-occurrence of contexts and take pro-active actions by activating previous models. The main benefit of this approach is that proposed meta-learner is capable of selecting similar historical concept, if there is one, without the knowledge of true classes of examples.
Czech name
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Czech description
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Publication year
2009
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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