Ensemble Representation Learning: An Analysis of Fitness and Survival for Wrapper-based Genetic Programming Methods

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

@InProceedings{LaCava:2017:GECCO,
  author =       "William {La Cava} and Jason H. Moore",
  title =        "Ensemble Representation Learning: An Analysis of
                 Fitness and Survival for Wrapper-based Genetic
                 Programming Methods",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4920-8",
  address =      "Berlin, Germany",
  pages =        "961--968",
  size =         "8 pages",
  URL =          "http://doi.acm.org/10.1145/3071178.3071215",
  DOI =          "doi:10.1145/3071178.3071215",
  acmid =        "3071215",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming,
                 classification, feature engineering, representation
                 learning",
  month =        "15-19 " # jul,
  abstract =     "Recently we proposed a general, ensemble-based feature
                 engineering wrapper (FEW) that was paired with a number
                 of machine learning methods to solve regression
                 problems. Here, we adapt FEW for supervised
                 classification and perform a thorough analysis of
                 fitness and survival methods within this framework. Our
                 tests demonstrate that two fitness metrics, one
                 introduced as an adaptation of the silhouette score,
                 outperform the more commonly used Fisher criterion. We
                 analyse survival methods and demonstrate that
                 epsilon-lexicase survival works best across our test
                 problems, followed by random survival which outperforms
                 both tournament and deterministic crowding. We conduct
                 a benchmark comparison to several classification
                 methods using a large set of problems and show that FEW
                 can improve the best classifier performance in several
                 cases. We show that FEW generates consistent,
                 meaningful features for a biomedical problem with
                 different ML pairings.",
  notes =        "Also known as \cite{LaCava:2017:ERL:3071178.3071215}
                 GECCO-2017 A Recombination of the 26th International
                 Conference on Genetic Algorithms (ICGA-2017) and the
                 22nd Annual Genetic Programming Conference (GP-2017)",
}

Genetic Programming entries for William La Cava Jason H Moore

Citations