  Decision tree ensembles are widely used in practice. In this work, we study
in ensemble settings the effectiveness of replacing the split strategy for the
state-of-the-art online tree learner, Hoeffding Tree, with a rigorous but more
eager splitting strategy that we had previously published as Hoeffding AnyTime
Tree. Hoeffding AnyTime Tree (HATT), uses the Hoeffding Test to determine
whether the current best candidate split is superior to the current split, with
the possibility of revision, while Hoeffding Tree aims to determine whether the
top candidate is better than the second best and if a test is selected, fixes
it for all posterity. HATT converges to the ideal batch tree while Hoeffding
Tree does not. We find that HATT is an efficacious base learner for online
bagging and online boosting ensembles. On UCI and synthetic streams, HATT as a
base learner outperforms HT within a 0.05 significance level for the majority
of tested ensembles on what we believe is the largest and most comprehensive
set of testbenches in the online learning literature. Our results indicate that
HATT is a superior alternative to Hoeffding Tree in a large number of ensemble
settings.

Machine Learning;Machine Learning