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An Empirical Quest for Optimal Rule Learning Heuristics
Type of publication: Techreport
Citation: jf:TUD-KE-2008-01
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-o ff by determining optimal parameter settings for five diff erent 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.
Authors Janssen, Frederik
F├╝rnkranz, Johannes
  • tud-ke-2008-01.pdf