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- W224701494 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 MultiobjectiveGenetic 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." @default.
- W224701494 created "2016-06-24" @default.
- W224701494 creator A5042449261 @default.
- W224701494 date "2002-07-01" @default.
- W224701494 modified "2023-09-27" @default.
- W224701494 title "Using Multiobjective Genetic Programming to Infer Logistic Polynomial Regression Models [and] Experimental Supplement" @default.
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