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RNTI

MODULAD
Caractérisation d'instances d'apprentissage pour un méta-mining évolutionnaire
In EGC 2016, vol. RNTI-E-30, pp.541-542
Abstract
Machine learning has proven to be a powerful tool in diverse fields, and is getting more
and more widely used by non-experts. One of the foremost difficulties they encounter lies
in the choice and calibration of the machine learning algorithm to use. Our objective is thus
to provide assistance in the matter, using a meta-learning approach based on an evolutionary
heuristic. We introduce here this approach as a potential solution to the limitation of current
data characterization.