First order random forests: Learning relational classifiers with complex aggregates |
| |
Authors: | Anneleen Van Assche Celine Vens Hendrik Blockeel Sašo Džeroski |
| |
Affiliation: | (1) Department of Computer Science, Katholieke Universiteit Leuven, Celestijnenlaan 200A, 3001 Leuven, Belgium;(2) Department of Knowledge Technologies, Jožef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia |
| |
Abstract: | In relational learning, predictions for an individual are based not only on its own properties but also on the properties
of a set of related individuals. Relational classifiers differ with respect to how they handle these sets: some use properties
of the set as a whole (using aggregation), some refer to properties of specific individuals of the set, however, most classifiers
do not combine both. This imposes an undesirable bias on these learners. This article describes a learning approach that avoids
this bias, using first order random forests. Essentially, an ensemble of decision trees is constructed in which tests are
first order logic queries. These queries may contain aggregate functions, the argument of which may again be a first order
logic query. The introduction of aggregate functions in first order logic, as well as upgrading the forest’s uniform feature
sampling procedure to the space of first order logic, generates a number of complications. We address these and propose a
solution for them. The resulting first order random forest induction algorithm has been implemented and integrated in the
ACE-ilProlog system, and experimentally evaluated on a variety of datasets. The results indicate that first order random forests
with complex aggregates are an efficient and effective approach towards learning relational classifiers that involve aggregates
over complex selections.
Editor: Rui Camacho |
| |
Keywords: | Relational learning Random forests Aggregation Decision tree learning |
本文献已被 SpringerLink 等数据库收录! |
|