A comparison of evolved linear and non-linear ensemble vote aggregators

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Stuart E. Lacy and Michael A. Lones and 
                 Stephen L. Smith",
  title =        "A comparison of evolved linear and non-linear ensemble
                 vote aggregators",
  booktitle =    "2015 IEEE Congress on Evolutionary Computation (CEC)",
  year =         "2015",
  pages =        "758--763",
  ISSN =         "1089-778X",
  month =        may,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CEC.2015.7256967",
  abstract =     "Ensemble classifiers have become a widely researched
                 area in machine learning because they are able to
                 generalise well to unseen data, making them suitable
                 for real world applications. Many approaches implement
                 simple voting techniques, such as majority voting or
                 averaging, to form the overall output. Alternatively,
                 Genetic Algorithms (GAs) can be used to train the
                 ensemble by optimising either binary or floating
                 weights in an average voting scheme to produce evolved
                 linear voting aggregators. Non-linear voting schemes
                 can also be formed by inputting the base classifiers'
                 outputs into a secondary classifier in the form of an
                 expression tree or an Artificial Neural Network; this
                 being subsequently evolved by an Evolutionary Algorithm
                 to optimise the ensemble prediction. This paper aims to
                 firstly, establish the impact of evolving linear
                 aggregators on traditional voting techniques, and
                 secondly whether these linear functions produce more
                 accurate predictions than more complex nonlinear
                 evolved aggregators. The results indicate that
                 optimising the ensemble combination method with GAs
                 offers significant advantages over standard approaches
                 and produces comparable ensembles to those with
                 nonlinear aggregators despite their additional

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