ID  - jf:TUD-KE-2008-01
T1  - An Empirical Quest for Optimal Rule Learning Heuristics
A1  - Janssen, Frederik
A1  - F├╝rnkranz, Johannes
Y1  - 2008
IS  - TUD-KE-2008-01
T2  - TU Darmstadt, Knowledge Engineering Group
UR  - http://www.ke.informatik.tu-darmstadt.de/publications/reports/tud-ke-2008-01.pdf
N2  - The primary goal of the research reported in this paper is to identify what criteria are
responsible for the good performance of a heuristic rule evaluation function in a greedy topdown covering algorithm. We rst argue that search heuristics for inductive rule learning
algorithms typically trade off consistency and coverage, and we investigate this trade-off by
determining optimal parameter settings for five different parametrized heuristics. In order
to avoid biasing our study by known functional families, we also investigate the potential of
using meta-learning for obtaining alternative rule learning heuristics. The key results of this
experimental study are not only practical default values for commonly used heuristics and a
broad comparative evaluation of known and novel rule learning heuristics, but we also gain
theoretical insights into factors that are responsible for a good performance. For example,
we observe that consistency should be weighed more heavily than coverage, presumably
because a lack of coverage can later be corrected by learning additional rules.
ER  -