We give a permutation approach to validation (estimation of out-sample error). One typical use of validation is model selection. We establish the legitimacy of the proposed permutation complexity by proving a uniform bound on the out-sample error, similar to a VC-style bound. We extensively demonstrate this approach experimentally on synthetic data, standard data sets from the UCI-repository, and a novel diffusion data set. The out-of-sample error estimates are comparable to cross validation (CV); yet, the method is more efficient and robust, being less susceptible to overfitting during model selection. Copyright © by SIAM.
CITATION STYLE
Magdon-Ismail, M., & Mertsalov, K. (2010). A permutation approach to validation. In Proceedings of the 10th SIAM International Conference on Data Mining, SDM 2010 (pp. 882–893). Society for Industrial and Applied Mathematics Publications. https://doi.org/10.1137/1.9781611972801.77
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