Using Multiobjective Genetic Programming to Infer Logistic Polynomial Regression Models

Created by W.Langdon from gp-bibliography.bib Revision:1.8081

@InProceedings{hunter:2002:ECAI,

• author = "Andrew Hunter",
• title = "Using Multiobjective Genetic Programming to Infer Logistic Polynomial Regression Models",
• booktitle = "15th European Conference on Artificial Intelligence",
• year = "2002",
• editor = "Frank {Van Harmelen}",
• pages = "193--197",
• address = "Lyon, France",
• month = "21-26 " # jul,
• organisation = "ECCAI and AFAI",
• publisher = "IOS Press",
• keywords = "genetic algorithms, genetic programming",
• URL = "http://frontiersinai.com/ecai/ecai2002/pdf/p0193.pdf",
• size = "5 pages",
• 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.",
• 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