Hybridizing Cartesian Genetic Programming and Harmony Search for adaptive feature construction in supervised learning problems

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@Article{Elola:2017:ASC,
  author =       "Andoni Elola and Javier {Del Ser} and 
                 Miren Nekane Bilbao and Cristina Perfecto and Enrique Alexandre and 
                 Sancho Salcedo-Sanz",
  title =        "Hybridizing Cartesian Genetic Programming and Harmony
                 Search for adaptive feature construction in supervised
                 learning problems",
  journal =      "Applied Soft Computing",
  volume =       "52",
  pages =        "760--770",
  year =         "2017",
  ISSN =         "1568-4946",
  DOI =          "doi:10.1016/j.asoc.2016.09.049",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1568494616305087",
  abstract =     "The advent of the so-called Big Data paradigm has
                 motivated a flurry of research aimed at enhancing
                 machine learning models by following very diverse
                 approaches. In this context this work focuses on the
                 automatic construction of features in supervised
                 learning problems, which differs from the conventional
                 selection of features in that new characteristics with
                 enhanced predictive power are inferred from the
                 original dataset. In particular this manuscript
                 proposes a new iterative feature construction approach
                 based on a self-learning meta-heuristic algorithm
                 (Harmony Search) and a solution encoding strategy
                 (correspondingly, Cartesian Genetic Programming) suited
                 to represent combinations of features by means of
                 constant-length solution vectors. The proposed feature
                 construction algorithm, coined as Adaptive Cartesian
                 Harmony Search (ACHS), incorporates modifications that
                 allow exploiting the estimated predictive importance of
                 intermediate solutions and, ultimately, attaining
                 better convergence rate in its iterative learning
                 procedure. The performance of the proposed ACHS scheme
                 is assessed and compared to that rendered by the state
                 of the art in a toy example and three practical use
                 cases from the literature. The excellent performance
                 figures obtained in these problems shed light on the
                 widespread applicability of the proposed scheme to
                 supervised learning with legacy datasets composed by
                 already refined characteristics.",
  keywords =     "genetic algorithms, genetic programming, Feature
                 construction, Supervised learning, Harmony Search",
}

Genetic Programming entries for Andoni Elola Javier Del Ser Miren Nekane Bilbao Cristina Perfecto Enrique Alexandre Sancho Salcedo-Sanz

Citations