Abstract
Genetic programming (GP) has proved successful at evolving pattern classifiers and although the paradigm lends itself easily to continuous pattern attributes, incorporating categorical attributes is little studied. Here we construct two synthetic datasets specifically to investigate the use of categorical attributes in GP and consider two possible approaches: indicator variables and integer mapping. We conclude that for ordered attributes, integer mapping yields the lowest errors. For purely nominal attributes, indicator variables give the best misclassification errors.
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Badran, K., Rockett, P. (2008). Integrating Categorical Variables with Multiobjective Genetic Programming for Classifier Construction. In: O’Neill, M., et al. Genetic Programming. EuroGP 2008. Lecture Notes in Computer Science, vol 4971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78671-9_26
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DOI: https://doi.org/10.1007/978-3-540-78671-9_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78670-2
Online ISBN: 978-3-540-78671-9
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