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.
Using Multiobjective Genetic Programming to Infer Logistic Polynomial Regression Models - Experimental Supplement | Using Multiobjective Genetic Programming to Infer Logistic Polynomial Regression Models - Experimental Supplement | | ![[img]](http://eprints.lincoln.ac.uk/style/images/fileicons/application_pdf.png) [Download] |
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Item Type: | Conference or Workshop contribution (Paper) |
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Item Status: | Live Archive |
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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.
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. |
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Keywords: | Algorithm, Genetic Programming, Non-linear classifiers, Neural Networks, Polynomial Modelling |
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Subjects: | G Mathematical and Computer Sciences > G730 Neural Computing |
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Divisions: | College of Science > School of Computer Science |
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ID Code: | 1899 |
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Deposited On: | 19 Jun 2009 10:22 |
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