Feature Selection and Classification Using Ensembles of Genetic Programs and Within-class and Between-class Permutations

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@InProceedings{Ivert:2015:CEC,
  author =       "Annica Ivert and Claus Aranha and Hitoshi Iba",
  title =        "Feature Selection and Classification Using Ensembles
                 of Genetic Programs and Within-class and Between-class
                 Permutations",
  booktitle =    "Proceedings of 2015 IEEE Congress on Evolutionary
                 Computation (CEC 2015)",
  year =         "2015",
  editor =       "Yadahiko Murata",
  pages =        "1121--1128",
  address =      "Sendai, Japan",
  month =        "25-28 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CEC.2015.7257015",
  abstract =     "Many feature selection methods are based on the
                 assumption that important features are highly
                 correlated with their corresponding classes, but mainly
                 uncorrelated with each other. Often, this assumption
                 can help eliminate redundancies and produce good
                 predictors using only a small subset of features.
                 However, when the predictability depends on
                 interactions between features, such methods will fail
                 to produce satisfactory results. In this paper a method
                 that can find important features, both independently
                 and dependently discriminative, is introduced. This
                 method works by performing two different types of
                 permutation tests that classify each of the features as
                 either irrelevant, independently predictive or
                 dependently predictive. It was evaluated using a
                 classifier based on an ensemble of genetic programs.
                 The attributes chosen by the permutation tests were
                 shown to yield classifiers at least as good as the ones
                 obtained when all attributes were used during training
                 - and often better. The proposed method also fared well
                 when compared to other attribute selection methods such
                 as RELIEFF and CFS. Furthermore, the ability to
                 determine whether an attribute was independently or
                 dependently predictive was confirmed using artificial
                 datasets with known dependencies.",
  notes =        "1005 hrs 15256 CEC2015",
}

Genetic Programming entries for Annica Ivert Claus de Castro Aranha Hitoshi Iba

Citations