Building Predictive Models via Feature Synthesis

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@InProceedings{Arnaldo:2015:GECCO,
  author =       "Ignacio Arnaldo and Una-May O'Reilly and 
                 Kalyan Veeramachaneni",
  title =        "Building Predictive Models via Feature Synthesis",
  booktitle =    "GECCO '15: Proceedings of the 2015 Annual Conference
                 on Genetic and Evolutionary Computation",
  year =         "2015",
  editor =       "Sara Silva and Anna I Esparcia-Alcazar and 
                 Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and 
                 Christine Zarges and Luis Correia and Terence Soule and 
                 Mario Giacobini and Ryan Urbanowicz and 
                 Youhei Akimoto and Tobias Glasmachers and 
                 Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and 
                 Marta Soto and Carlos Cotta and Francisco B. Pereira and 
                 Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and 
                 Heike Trautmann and Jean-Baptiste Mouret and 
                 Sebastian Risi and Ernesto Costa and Oliver Schuetze and 
                 Krzysztof Krawiec and Alberto Moraglio and 
                 Julian F. Miller and Pawel Widera and Stefano Cagnoni and 
                 JJ Merelo and Emma Hart and Leonardo Trujillo and 
                 Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and 
                 Carola Doerr",
  isbn13 =       "978-1-4503-3472-3",
  pages =        "983--990",
  keywords =     "genetic algorithms, genetic programming",
  month =        "11-15 " # jul,
  organisation = "SIGEVO",
  address =      "Madrid, Spain",
  URL =          "http://doi.acm.org/10.1145/2739480.2754693",
  DOI =          "doi:10.1145/2739480.2754693",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "We introduce Evolutionary Feature Synthesis (EFS), a
                 regression method that generates readable, nonlinear
                 models of small to medium size datasets in seconds. EFS
                 is, to the best of our knowledge, the fastest
                 regression tool based on evolutionary computation
                 reported to date. The feature search involved in the
                 proposed method is composed of two main steps: feature
                 composition and feature subset selection. EFS adopts a
                 bottom-up feature composition strategy that eliminates
                 the need for a symbolic representation of the features
                 and exploits the variable selection process involved in
                 pathwise regularized linear regression to perform the
                 feature subset selection step. The result is a
                 regression method that is competitive against neural
                 networks, and outperforms both linear methods and
                 Multiple Regression Genetic Programming, up to now the
                 best regression tool based on evolutionary
                 computation.",
  notes =        "Also known as \cite{2754693} GECCO-2015 A joint
                 meeting of the twenty fourth international conference
                 on genetic algorithms (ICGA-2015) and the twentith
                 annual genetic programming conference (GP-2015)",
}

Genetic Programming entries for Ignacio Arnaldo Lucas Una-May O'Reilly Kalyan Veeramachaneni

Citations