Abstract
In certain problem domains, “The Curse of Dimensionality” (Hastie et al., 2001) is well known. Also known as the problem of “High P and Low N” where the number of parameters far exceeds the number of samples to learn from, we describe our methods for making the most of limited samples in producing reasonably general classification rules from data with a larger number of parameters. We discuss the application of this approach in classifying mesothelioma samples from baseline data according to their time to recurrence. In this case there are 12,625 inputs for each sample but only 19 samples to learn from. We reflect on the theoretical implications of the behavior of GP in these extreme cases and speculate on the nature of generality.
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Worzel, W.P., Almal, A., MacLean, C.D. (2007). Lifting the Curse of Dimensionality. In: Riolo, R., Soule, T., Worzel, B. (eds) Genetic Programming Theory and Practice IV. Genetic and Evolutionary Computation. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-49650-4_3
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DOI: https://doi.org/10.1007/978-0-387-49650-4_3
Publisher Name: Springer, Boston, MA
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