NDLI: Scaling Genetic Programming to Large Datasets Using Hierarchical Dynamic Subset Selection

Content Provider	IEEE Xplore Digital Library
Author	Curry, R. Lichodzijewski, P. Heywood, M.I.
Copyright Year	1996
Abstract	The computational overhead of genetic programming (GP) may be directly addressed without recourse to hardware solutions using active learning algorithms based on the random or dynamic subset selection heuristics (RSS or DSS). This correspondence begins by presenting a family of hierarchical DSS algorithms: RSS-DSS, cascaded RSS-DSS, and the balanced block DSS algorithm, where the latter has not been previously introduced. Extensive benchmarking over four unbalanced real-world binary classification problems with 30000-500000 training exemplars demonstrates that both the cascade and balanced block algorithms are able to reduce the likelihood of degenerates while providing a significant improvement in classification accuracy relative to the original RSS-DSS algorithm. Moreover, comparison with GP trained without an active learning algorithm indicates that classification performance is not compromised, while training is completed in minutes as opposed to half a day.
Page Count	9
File Size	311805
Starting Page	1065
Ending Page	1073
File Format	PDF
ISSN	10834419
Volume Number	37
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Genetic programming Dynamic programming Decision support systems Hardware Machine learning algorithms Machine learning Computational efficiency Stochastic processes Scholarships Classification algorithms unbalanced datasets Active learning classification genetic programming (GP) large datasets
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Information Systems Electrical and Electronic Engineering Human-Computer Interaction Computer Science Applications Software

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