Relational learning and boosting

Authors:
Ross Quinlan
Affiliations:
-
Venue:
Relational Data Mining
Year:
2001

Citing 16
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Abstract

Boosting, a methodology for constructing and combining multiple classifiers, has been found to lead to substantial improvements in predictive accuracy. Although boosting was formulated in a propositional learning context, the same ideas can be applied to first-order learning (also known as inductive logic programming). Boosting is used here with a system that learns relational definitions of functions. Results show that the occasional negative impact of boosting all resemble the corresponding observations for propositional learning.