Forming classifier ensembles with multimodal evolutionary algorithms

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@InProceedings{lacy2015forming,
  title =        "Forming classifier ensembles with multimodal
                 evolutionary algorithms",
  author =       "Stuart E. Lacy and Michael A. Lones and 
                 Stephen L. Smith",
  booktitle =    "2015 IEEE Congress on Evolutionary Computation (CEC)",
  year =         "2015",
  pages =        "723--729",
  month =        "25-28 " # may,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1089-778X",
  DOI =          "doi:10.1109/CEC.2015.7256962",
  size =         "7 pages",
  abstract =     "Ensemble classifiers have become popular in recent
                 years owing to their ability to produce robust
                 predictive models that generalise well to previously
                 unseen data. In principle, Evolutionary Algorithms
                 (EAs) are well suited to ensemble generation since they
                 result in a pool of trained classifiers. However, in
                 practice they are infrequently used for this purpose.
                 Current research trends in the EA community focus on
                 relatively complex mechanisms for building ensembles,
                 such as co-evolution and multi-objective optimisation.
                 In this paper, we take a back-to-basics approach,
                 studying whether conventional EAs, augmented with
                 simple niching strategies, can be used to form accurate
                 ensembles. We focus on crowding for this, considering
                 both deterministic and probabilistic variants. We also
                 consider the effect of different similarity measures.
                 Our results suggest that simple niching methods can
                 lead to accurate ensemble classifiers and that the
                 choice of similarity measure is not a significant
                 factor. A further study using heterogeneous classifier
                 models within the population showed no added benefit.",
}

Genetic Programming entries for Stuart E Lacy Michael A Lones Stephen L Smith

Citations