Lifting the Curse of Dimensionality

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@InCollection{Worzel:2006:GPTP,

• author = "W. P. Worzel and A. Almal and C. D. MacLean",
• title = "Lifting the Curse of Dimensionality",
• booktitle = "Genetic Programming Theory and Practice {IV}",
• year = "2006",
• editor = "Rick L. Riolo and Terence Soule and Bill Worzel",
• volume = "5",
• series = "Genetic and Evolutionary Computation",
• pages = "29--40",
• address = "Ann Arbor",
• month = "11-13 " # may,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "0-387-33375-4",
• DOI = "doi:10.1007/978-0-387-49650-4_3",
• 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 over 12625 inputs for each sample but only 19 samples to learn from. We reflect on the theoretical implications of the behaviour of GP in these extreme cases and speculate on the nature of generality.",
• notes = "part of \cite{Riolo:2006:GPTP} Published Jan 2007 after the workshop",

}

Genetic Programming entries for William P Worzel Arpit A Almal Duncan MacLean

Citations