Inferencing Bayesian Networks from Time Series Data Using Natural Selection
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{DBLP:conf/flairs/NovobilskiK00,
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author = "Andrew J. Novobilski and Farhad A. Kamangar",
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title = "Inferencing Bayesian Networks from Time Series Data
Using Natural Selection",
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booktitle = "Proceedings of the Thirteenth International Florida
Artificial Intelligence Research Society Conference",
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year = "2000",
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editor = "James N. Etheredge and Bill Z. Manaris",
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pages = "298--302",
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address = "Orlando, Florida, USA",
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month = may # " 22-24",
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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 = "1-57735-113-4",
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URL = "http://www.aaai.org/Papers/FLAIRS/2000/FLAIRS00-056.pdf",
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size = "5 pages",
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abstract = "This paper describes a new framework for using natural
selection to evolve Bayesian Networks for use in
forecasting time series data. It extends current
research by introducing a tree based representation of
a candidate Bayesian Network that addresses the problem
of model identification and training through the use of
natural selection. The framework constructs a modified
Naive Bayesian classifier by searching for
relationships within the data that will produce a model
for the underlying process generating the time series
data. Experimental results are presented that compare
forecasts in the presence of multiple sources of
information made using the naturally selected belief
network versus a random walk.",
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notes = "FLAIRS 2000 Conference
http://www.aaai.org/Library/FLAIRS/flairs00contents.php",
- }
Genetic Programming entries for
Andrew J Novobilski
Farhad A Kamangar
Citations