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GECCO
2006
Springer

Improving GP classifier generalization using a cluster separation metric

11 years 5 months ago
Improving GP classifier generalization using a cluster separation metric
Genetic Programming offers freedom in the definition of the cost function that is unparalleled among supervised learning algorithms. However, this freedom goes largely unexploited in previous work. Here, we revisit the design of fitness functions for genetic programming by explicitly considering the contribution of the wrapper and cost function. Within the context of supervised learning, as applied to classification problems, a clustering methodology is introduced using cost functions which encourage maximization of separation between in and out of class exemplars. Through a series of empirical investigations of the nature of these functions, we demonstrate that classifier performance is much more dependable than previously the case under the genetic programming paradigm. Categories and Subject Descriptors I.2.2 [Artificial Intelligence]: Automatic Programming General Terms Algorithms, Experimentation, Performance Keywords genetic programming, clustering, classification, evaluation
Ashley George, Malcolm I. Heywood
Added 23 Aug 2010
Updated 23 Aug 2010
Type Conference
Year 2006
Where GECCO
Authors Ashley George, Malcolm I. Heywood
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