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

Hunter, Andrew (2002) Using Multiobjective Genetic Programming to Infer Logistic Polynomial Regression Models [and] Experimental Supplement. In: 15th European Conference on Artificial Intelligence 2002, 21-26 July 2002, Lyon, France.

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Using Multiobjective Genetic Programming to Infer Logistic Polynomial Regression Models - Experimental Supplement
Using Multiobjective Genetic Programming to Infer Logistic Polynomial Regression Models - Experimental Supplement
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Using Multiobjective Genetic Programming to Infer Logistic Polynomial Regression Models
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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
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.

Item Type:Conference or Workshop Item (Paper)
Additional Information: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.
Keywords:Algorithm, Genetic Programming, Non-linear classifiers, Neural Networks, Polynomial Modelling
Subjects:G Mathematical and Computer Sciences > G730 Neural Computing
Divisions:College of Science > School of Computer Science
ID Code:1899
Deposited By: Tammie Farley
Deposited On:19 Jun 2009 10:22
Last Modified:13 Mar 2013 08:32

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