We evaluated the algorithms on a variety of reasonably large and
noise-free training sets from the UCI collection of Machine Learning
databases. As our implementation can only handle 2-class problems, we
constructed a binary version of the multi-class *Shuttle* domain
by discriminating examples of majority class from all other
classes. In the KRK illegality domain we used a propositional version
of the original relational learning problem [46], where
each position is encoded with features that correspond to the truth
values of the 18 different meaningful instantiations of the `adjacent`, `equal`, and `less_than` relations in the
background knowledge.

Table 2 shows the total number of examples available
for each domain and the ratio of the average run-time of C4.5 with
windowing (invoked using the parameter setting `-t 1`) versus C4.5
without windowing. The last column shows the redundancy of the domain,
estimated with Møller's conditional population entropy
heuristic (2). Interestingly enough, there seems to be a
(negative) correlation between the performance of C4.5's windowing
algorithm and this redundancy measure.^{10} In general, the results with C4.5 confirm
the results of [59] that not much can be gained with the
use of windowing for ID3-like learners. The only exception is the *Shuttle* domain, where windowing can save almost half of C4.5's
run-time.