Cascaded GP Models for Data Mining

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@InProceedings{lichodzijewski:2004:cgmfdm,

• author = "Peter Lichodzijewski and Nur Zincir-Heywood and Malcolm Heywood",
• title = "Cascaded GP Models for Data Mining",
• pages = "2258--2264",
• booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary Computation",
• year = "2004",
• publisher = "IEEE Press",
• month = "20-23 " # jun,
• address = "Portland, Oregon",
• ISBN = "0-7803-8515-2",
• URL = "http://flame.cs.dal.ca/~piotr/01331178.pdf",
• DOI = "doi:10.1109/CEC.2004.1331178",
• keywords = "genetic algorithms, genetic programming",
• 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.",
• 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