Using Multiobjective Genetic Programming to Infer Logistic Polynomial Regression Models
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{hunter:2002:ECAI,
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author = "Andrew Hunter",
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title = "Using Multiobjective Genetic Programming to Infer
Logistic Polynomial Regression Models",
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booktitle = "15th European Conference on Artificial Intelligence",
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year = "2002",
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editor = "Frank {Van Harmelen}",
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pages = "193--197",
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address = "Lyon, France",
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month = "21-26 " # jul,
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organisation = "ECCAI and AFAI",
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publisher = "IOS Press",
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keywords = "genetic algorithms, genetic programming",
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URL = "http://frontiersinai.com/ecai/ecai2002/pdf/p0193.pdf",
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size = "5 pages",
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abstract = "In designing non-linear classifiers, there are
important trade-offs to be made between predictive
accuracy and model comprehensibility or complexity. We
introduce the use of Genetic Programming to generate
logistic polynomial models, a relatively comprehensible
non-linear parametric model; describe an efficient
twostage algorithm consisting of GP structure design
and Quasi-Newton coefficient setting; demonstrate that
Niched Pareto Multiobjective Genetic Programming can be
used to discover a range of classifiers with different
complexity versus 'performance' trade-offs; introduce a
technique to integrate a new 'ROC (Receiver Operating
Characteristic) dominance' concept into the
multiobjective setting; and suggest some modifications
to the Niched Pareto GA for use in Genetic Programming.
The technique successfully generates classifiers with
diverse complexity and performance characteristics.",
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notes = "ECAI 2002
http://frontiersinai.com/ecai/ecai2002/index.html
broken Dec 2012
http://ecai2002.univ-lyon1.fr/show_en.pl?page=en/program/ecai.html
PAIS 2012 Boi Faltings",
- }
Genetic Programming entries for
Andrew Hunter
Citations