25 February 1999 Concurrent approach for evolving compact decision rule sets
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Abstract
The induction of decision rules from data is important to many disciplines, including artificial intelligence and pattern recognition. To improve the state of the art in this area, we introduced the genetic rule and classifier construction environment (GRaCCE). It was previously shown that GRaCCE consistently evolved decision rule sets from data, which were significantly more compact than those produced by other methods (such as decision tree algorithms). The primary disadvantage of GRaCCe, however, is its relatively poor run-time execution performance. In this paper, a concurrent version of the GRaCCE architecture is introduced, which improves the efficiency of the original algorithm. A prototype of the algorithm is tested on an in- house parallel processor configuration and the results are discussed.
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Robert E. Marmelstein, Lonnie P. Hammack, Gary B. Lamont, "Concurrent approach for evolving compact decision rule sets", Proc. SPIE 3695, Data Mining and Knowledge Discovery: Theory, Tools, and Technology, (25 February 1999); doi: 10.1117/12.339990; https://doi.org/10.1117/12.339990
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