Model Complexity and Algorithm Selection in Classification

Authors:
Melanie Hilario
Affiliations:
-
Venue:
DS '02 Proceedings of the 5th International Conference on Discovery Science
Year:
2002

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Abstract

Building an effective classifer involves choosing the model class with the appropriate learning bias as well as the right level of complexity within that class. These two aspects have rarely been addressed together: typically, model class (or algorithm) selection is performed on the basis of default settings, while model instance (or complexity) selection is investigated within the confines of a single model class. We study the impact of model complexity on algorithm selection and show how the relative performance of candidate algorithms changes drastically with the choice of complexity parameters.