Enhancing Discrimination Power with Genetic Feature Construction: A Grammatical Evolution Approach

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Miquilini:2016:CEC,
  author =       "Patricia Miquilini and Rodrigo C. Barros and 
                 Vinicius V {de Melo} and Marcio P. Basgalupp",
  title =        "Enhancing Discrimination Power with Genetic Feature
                 Construction: A Grammatical Evolution Approach",
  booktitle =    "Proceedings of 2016 IEEE Congress on Evolutionary
                 Computation (CEC 2016)",
  year =         "2016",
  editor =       "Yew-Soon Ong",
  pages =        "3824--3831",
  address =      "Vancouver",
  month =        "24-29 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 Evolution",
  isbn13 =       "978-1-5090-0623-6",
  DOI =          "doi:10.1109/CEC.2016.7744274",
  abstract =     "Data set preprocessing is a critical step for the
                 successful application of machine learning algorithms
                 in classification tasks. Even though we rely on
                 learning algorithms to pinpoint the optimal decision
                 boundaries in the feature space by properly detecting
                 latent relationships among the input features, their
                 performance is often bounded by the discriminative
                 power of the available features. Therefore, much effort
                 has been devoted to developing preprocessing methods
                 that are capable of transforming the input data with
                 the final goal of aiding the machine learning algorithm
                 in building high-quality classification models. One
                 such a method is feature construction, which is a
                 flexible preprocessing procedure that exploits linear
                 and nonlinear transformations of the original feature
                 space in an attempt to capture useful information that
                 is not explicit in the original data. Since the task of
                 feature construction can be modelled as a heuristic
                 search in the space of novel latent features, this
                 paper investigates an evolutionary approach for
                 performing such a task, namely grammatical evolution
                 (GE). In our proposed approach, GE is employed for
                 building an extra novel feature from the available
                 input data in order to maximize the predictive
                 performance of the learning algorithm in training data.
                 Results show that many interesting implicit
                 relationships are indeed found by the evolutionary
                 approach, improving the performance of two well known
                 decision-tree induction algorithms.",
  notes =        "WCCI2016",
}

Genetic Programming entries for Patricia Miquilini Rodrigo C Barros Vinicius Veloso de Melo Marcio Porto Basgalupp

Citations