Translator Disclaimer
25 February 1999 Concurrent approach for evolving compact decision rule sets
Author Affiliations +
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.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Robert E. Marmelstein, Lonnie P. Hammack, and 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);


Artificial intelligence tools for pattern recognition
Proceedings of SPIE (June 19 2017)
Dot pattern clustering using a cellular neural network
Proceedings of SPIE (February 01 1998)
Feature selection in bioinformatics
Proceedings of SPIE (May 10 2012)
A step toward the foundations of data mining
Proceedings of SPIE (March 21 2003)
Is mining of knowledge possible?
Proceedings of SPIE (March 17 2008)

Back to Top