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Using Multiobjective Genetic Programming to Infer Logistic Polynomial Regression Models [and] Experimental Supplement

By Andrew Hunter

Abstract

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\ud 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

Topics: G730 Neural Computing
Year: 2002
OAI identifier: oai:eprints.lincoln.ac.uk:1899

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  1. (2002). Using multiobjective genetic programming to infer logistic polynomial regression models’,

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