%Aigaion2 BibTeX export from Knowledge Engineering Publications
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@TECHREPORT{jf:TUD-KE-2008-01,
       author = {Janssen, Frederik and F{\"{u}}rnkranz, Johannes},
        title = {An Empirical Quest for Optimal Rule Learning Heuristics},
       number = {TUD-KE-2008-01},
         year = {2008},
  institution = {TU Darmstadt, Knowledge Engineering Group},
          url = {http://www.ke.informatik.tu-darmstadt.de/publications/reports/tud-ke-2008-01.pdf},
     abstract = {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.}
}