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RNTI

MODULAD
Arbres de modèles et flux de données incomplets
In EGC 2016, vol. RNTI-E-30, pp.519-520
Résumé
Model tree is a useful and convenient method for predictive analytics in data streams.
Often, this issue is solved by pre-processing techniques applied prior to the training phase of
the model. In this article, we propose a new method that estimates and adjusts missing values
before the model tree training. A prototype was developed and tested on several data streams.