  A reported weakness of C4.5 in domains with continuous attributes is
addressed by modifying the formation and evaluation of tests on continuous
attributes. An MDL-inspired penalty is applied to such tests, eliminating some
of them from consideration and altering the relative desirability of all tests.
Empirical trials show that the modifications lead to smaller decision trees
with higher predictive accuracies. Results also confirm that a new version of
C4.5 incorporating these changes is superior to recent approaches that use
global discretization and that construct small trees with multi-interval
splits.
