15th European Conference on Artificial Intelligence
|July 21-26 2002 Lyon France|
In designing non-linear classifiers, there are important trade-offs to be made between predictive accuracy and model interpretability or complexity. We introduce the use of Genetic Programming to generate logistic polynomial models, a relatively interpretable non-linear parametric model; describe an efficient algorithm based on a division into 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 dominance'' (Receiver Operating Characteristic performance) 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: Genetic Algorithms, Data Mining and Knowledge Discovery
Citation: Andrew Hunter: Using Multiobjective Genetic Programming to Infer Logistic Polynomial Regression Models. In F. van Harmelen (ed.): ECAI2002, Proceedings of the 15th European Conference on Artificial Intelligence, IOS Press, Amsterdam, 2002, pp.193-197.