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: | /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. |
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