Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming

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@Article{Ojha:2017:ASC,
  author =       "Varun Kumar Ojha and Ajith Abraham and Vaclav Snasel",
  title =        "Ensemble of heterogeneous flexible neural trees using
                 multiobjective genetic programming",
  journal =      "Applied Soft Computing",
  volume =       "52",
  pages =        "909--924",
  year =         "2017",
  ISSN =         "1568-4946",
  DOI =          "doi:10.1016/j.asoc.2016.09.035",
  URL =          "http://www.sciencedirect.com/science/article/pii/S156849461630494X",
  abstract =     "Machine learning algorithms are inherently
                 multiobjective in nature, where approximation error
                 minimization and model's complexity simplification are
                 two conflicting objectives. We proposed a
                 multiobjective genetic programming (MOGP) for creating
                 a heterogeneous flexible neural tree (HFNT), tree-like
                 flexible feedforward neural network model. The
                 functional heterogeneity in neural tree nodes was
                 introduced to capture a better insight of data during
                 learning because each input in a dataset possess
                 different features. MOGP guided an initial HFNT
                 population towards Pareto-optimal solutions, where the
                 final population was used for making an ensemble
                 system. A diversity index measure along with
                 approximation error and complexity was introduced to
                 maintain diversity among the candidates in the
                 population. Hence, the ensemble was created by using
                 accurate, structurally simple, and diverse candidates
                 from MOGP final population. Differential evolution
                 algorithm was applied to fine-tune the underlying
                 parameters of the selected candidates. A comprehensive
                 test over classification, regression, and time-series
                 datasets proved the efficiency of the proposed
                 algorithm over other available prediction methods.
                 Moreover, the heterogeneous creation of HFNT proved to
                 be efficient in making ensemble system from the final
                 population.",
  keywords =     "genetic algorithms, genetic programming, Pareto-based
                 multiobjectives, Flexible neural tree, Ensemble,
                 Approximation, Feature selection",
}

Genetic Programming entries for Varun Kumar Ojha Ajith Abraham Vaclav Snasel

Citations