Absolute Percent Error Based Fitness Functions for Evolving Forecast Models
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{DBLP:conf/flairs/NovobilskiK01,
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author = "Andy Novobilski and Farhad A. Kamangar",
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title = "Absolute Percent Error Based Fitness Functions for
Evolving Forecast Models",
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booktitle = "Proceedings of the Fourteenth International Florida
Artificial Intelligence Research Society Conference",
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year = "2001",
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editor = "Ingrid Russell and John F. Kolen",
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pages = "591--595",
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address = "Key West, Florida, USA",
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month = may # " 21-23",
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publisher = "AAAI Press",
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bibsource = "DBLP, http://dblp.uni-trier.de",
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keywords = "genetic algorithms, genetic programming, Bayesian
Networks, datamining",
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ISBN = "1-57735-133-9",
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URL = "http://www.aaai.org/Papers/FLAIRS/2001/FLAIRS01-113.pdf",
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size = "5 pages",
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abstract = "One aspect of evolutionary computing as a method of
data mining, is its intrinsic ability to drive model
selection according to a mixed set of criteria. Based
on natural selection, evolutionary computing uses
evaluation of candidate solutions according to a
fitness criteria that might or might not share the
exact same implementation as the metric used to measure
the performance of the selected solution. This paper
presents the results of using four different fitness
functions to evolve naive Bayesian networks based on a
combination of Mean Absolute Percent Error and Worst
Absolute Percent Error values for individual population
members. In addition to the error measurements from
both the training and forecast evaluations, data is
presented that shows APE for individual members during
the forecast generation and evaluation phase",
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notes = "FLAIRS 2001 Conference
http://www.aaai.org/Press/Proceedings/flairs01.php",
- }
Genetic Programming entries for
Andrew J Novobilski
Farhad A Kamangar
Citations