Integer-based genetic algorithm for feature selection in multivariate calibration

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Rhelcris S. Sousa and Telma W. {de Lima} and 
                 Lauro C. M. {de Paula} and Roney L. Lima and 
                 Arlindo R. G. Filho and Anderson S. Soares",
  booktitle =    "2017 IEEE Congress on Evolutionary Computation (CEC)",
  title =        "Integer-based genetic algorithm for feature selection
                 in multivariate calibration",
  year =         "2017",
  editor =       "Jose A. Lozano",
  pages =        "2315--2320",
  address =      "Donostia, San Sebastian, Spain",
  publisher =    "IEEE",
  month =        "5-8 " # jun,
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, feature selection, integer programming, 2014
                 IDRC, binary encoding, dimensionality reduction,
                 evolutionary algorithms, gas mixtures, integer-based GA
                 implementation, integer-based genetic algorithm, model
                 prediction error, multivariate calibration models,
                 petroleum reservoirs, Calibration, Encoding,
                 Mathematical model, Predictive models, Sociology,
  isbn13 =       "978-1-5090-4601-0",
  DOI =          "doi:10.1109/CEC.2017.7969585",
  abstract =     "Feature selection is a important task to reduce
                 dimensionality in large datasets. Datasets from
                 multivariate calibration problems are a good example of
                 datasets with a large number of features. In
                 literature, there are several types of techniques to
                 reduce the number of features for this problem, among
                 them, evolutionary algorithms such as genetic
                 algorithms (GAs). They have been successfully used with
                 binary encoding to select features in multivariate
                 calibration. However, as far as we know, there is no
                 work in literature which provides an integer encoding
                 GA in such context. Thus, this paper presents an
                 integer-based GA implementation for feature selection
                 in multivariate calibration models. The results
                 demonstrated that our proposal is able to outperform
                 the outcomes of participants from 2014 IDRC regarding
                 model prediction error as well as number of selected
                 features. In this dataset, the samples correspond to
                 oils from petroleum reservoirs around the world and gas
                 mixtures in the gas phase measured in transmittance.
                 The gain of our proposed implementation in relation to
                 the winner was from 20.9percent up to 88.8percent.",
  notes =        "Is this GP?

                 IEEE Catalog Number: CFP17ICE-ART Also known as

Genetic Programming entries for Rhelcris S Sousa Telma W de Lima Lauro C M de Paula Roney L Lima Arlindo R G Filho Anderson S Soares