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Genetic Programming for Data Classification: Partitioning the Search Space

, , and . Proceedings of the 2004 Symposium on Applied Computing (ACM SAC'04), page 1001--1005. Nicosia, Cyprus, (14-17 March 2004)

Abstract

When Genetic Programming is used to evolve decision trees for data classification, search spaces tend to become extremely large. We present several methods using techniques from the field of machine learning to refine and thereby reduce the search space sizes for decision tree evolvers. We will show that these refinement methods improve the classification performance of our algorithms.

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