Journal
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
Volume 10, Issue 3, Pages -Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/2829955
Keywords
Data streams; regression; rule learning
Funding
- project ERDF through the COMPETE Programme
- National Funds through Fundacao para a Ciencia e a Tecnologia (FCT) [FCOMP - 01-0124-FEDER-022701]
- European Commission through the project MAESTRA [ICT-750 2013-612944]
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Decision rules are one of the most expressive and interpretable models for machine learning. In this article, we present Adaptive Model Rules (AMRules), the first stream 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 the attributes. In order to maintain a regression model compatible with the most recent state of the process generating data, each rule uses a Page-Hinkley test to detect changes in this process and react to changes by pruning the rule set. Online learning might be strongly affected by outliers. AMRules is also equipped with outliers detection mechanisms to avoid model adaption using anomalous examples. 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.
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