A Preliminary Investigation of Overfitting in Evolutionary Driven Model Induction: Implications for Financial Modelling

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@InProceedings{tuite:evoapps11,
  author =       "Cliodhna Tuite and Alexandros Agapitos and 
                 Michael O'Neill and Anthony Brabazon",
  title =        "A Preliminary Investigation of Overfitting in
                 Evolutionary Driven Model Induction: Implications for
                 Financial Modelling",
  booktitle =    "Applications of Evolutionary Computing,
                 EvoApplications 2011: {EvoCOMNET}, {EvoFIN}, {EvoHOT},
                 {EvoMUSART}, {EvoSTIM}, {EvoTRANSLOG}",
  year =         "2011",
  month =        "27-29 " # apr,
  editor =       "Cecilia {Di Chio} and Anthony Brabazon and 
                 Gianni {Di Caro} and Rolf Drechsler and Marc Ebner and 
                 Muddassar Farooq and Joern Grahl and Gary Greenfield and 
                 Christian Prins and Juan Romero and 
                 Giovanni Squillero and Ernesto Tarantino and Andrea G. B. Tettamanzi and 
                 Neil Urquhart and A. Sima Uyar",
  series =       "LNCS",
  volume =       "6625",
  publisher =    "Springer Verlag",
  address =      "Turin, Italy",
  publisher_address = "Berlin",
  pages =        "120--130",
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution",
  isbn13 =       "978-3-642-20519-4",
  DOI =          "doi:10.1007/978-3-642-20520-0_13",
  size =         "11 pages",
  abstract =     "This paper investigates the effects of early stopping
                 as a method to counteract overfitting in evolutionary
                 data modelling using Genetic Programming. Early
                 stopping has been proposed as a method to avoid model
                 over training, which has been shown to lead to a
                 significant degradation of out-of-sample performance.
                 If we assume some sort of performance metric
                 maximisation, the most widely used early training
                 stopping criterion is the moment within the learning
                 process that an unbiased estimate of the performance of
                 the model begins to decrease after a strictly monotonic
                 increase through the earlier learning iterations. We
                 are conducting an initial investigation on the effects
                 of early stopping in the performance of Genetic
                 Programming in symbolic regression and financial
                 modelling. Empirical results suggest that early
                 stopping using the above criterion increases the
                 extrapolation abilities of symbolic regression models,
                 but is by no means the optimal training-stopping
                 criterion in the case of a real-world financial
                 dataset.",
  notes =        "Part of \cite{DiChio:2011:evo_b} EvoApplications2011
                 held inconjunction with EuroGP'2011, EvoCOP2011 and
                 EvoBIO2011",
}

Genetic Programming entries for Cliodhna Tuite Alexandros Agapitos Michael O'Neill Anthony Brabazon

Citations