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Title: Adaptive model rules from data streams
Authors: Ezilda Duarte Almeida
Carlos Ferreira
João Gama
Issue Date: 2013
Abstract: Decision rules are one of the most expressive languages for machine learning. In this paper we present Adaptive Model Rules (AMRules), the first streaming rule learning algorithm for regression problems. In AMRules the antecedent of a rule is a conjunction of conditions on the attribute values, and the consequent is a linear combination of attribute values. Each rule uses a Page-Hinkley test to detect changes in the process generating data and react to changes by pruning the rule set. In the experimental section we report the results of AMRules on benchmark regression problems, and compare the performance of our system with other streaming regression algorithms. © 2013 Springer-Verlag.
metadata.dc.type: conferenceObject
Appears in Collections:CRACS - Other Publications
LIAAD - Other Publications

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