Recurrent concepts in data streams classification
Recurrent concepts in data streams classification
dc.contributor.author | João Gama | en |
dc.contributor.author | Kosina,P | en |
dc.date.accessioned | 2017-11-20T10:47:08Z | |
dc.date.available | 2017-11-20T10:47:08Z | |
dc.date.issued | 2014 | en |
dc.description.abstract | 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. | en |
dc.identifier.uri | http://repositorio.inesctec.pt/handle/123456789/3610 | |
dc.identifier.uri | http://dx.doi.org/10.1007/s10115-013-0654-6 | en |
dc.language | eng | en |
dc.relation | 5120 | en |
dc.rights | info:eu-repo/semantics/openAccess | en |
dc.title | Recurrent concepts in data streams classification | en |
dc.type | article | en |
dc.type | Publication | en |