Cascaded GP Models for Data Mining
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{lichodzijewski:2004:cgmfdm,
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author = "Peter Lichodzijewski and Nur Zincir-Heywood and
Malcolm Heywood",
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title = "Cascaded GP Models for Data Mining",
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pages = "2258--2264",
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booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary
Computation",
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year = "2004",
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publisher = "IEEE Press",
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month = "20-23 " # jun,
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address = "Portland, Oregon",
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ISBN = "0-7803-8515-2",
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URL = "http://flame.cs.dal.ca/~piotr/01331178.pdf",
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DOI = "doi:10.1109/CEC.2004.1331178",
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keywords = "genetic algorithms, genetic programming",
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abstract = "The Cascade Architecture for incremental learning is
demonstrated within the context of Genetic Programming.
Such a scheme provides the basis for building steadily
more complex models until a desired degree of accuracy
is reached. The architecture is demonstrated for
several data mining datasets. Efficient training on
standard computing platforms is retained through the
use of the RSS-DSS algorithm for stochastically
sampling datasets in proportion to exemplar
'difficulty' and 'age'. Finally, the ensuing empirical
study provides the basis for recommending the utility
of sum square cost functions in the datasets
considered.",
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notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and
the IEE.",
- }
Genetic Programming entries for
Peter Lichodzijewski
Nur Zincir-Heywood
Malcolm Heywood
Citations