Maximum Margin Decision Surfaces for Increased Generalisation in Evolutionary Decision Tree Learning

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

  author =       "Alexandros Agapitos and Michael O'Neill and 
                 Anthony Brabazon and Theodoros Theodoridis",
  title =        "Maximum Margin Decision Surfaces for Increased
                 Generalisation in Evolutionary Decision Tree Learning",
  booktitle =    "Proceedings of the 14th European Conference on Genetic
                 Programming, EuroGP 2011",
  year =         "2011",
  month =        "27-29 " # apr,
  editor =       "Sara Silva and James A. Foster and Miguel Nicolau and 
                 Mario Giacobini and Penousal Machado",
  series =       "LNCS",
  volume =       "6621",
  publisher =    "Springer Verlag",
  address =      "Turin, Italy",
  pages =        "61--72",
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-20406-7",
  DOI =          "doi:10.1007/978-3-642-20407-4_6",
  abstract =     "Decision tree learning is one of the most widely used
                 and practical methods for inductive inference. We
                 present a novel method that increases the
                 generalisation of genetically-induced classification
                 trees, which employ linear discriminants as the
                 partitioning function at each internal node. Genetic
                 Programming is employed to search the space of oblique
                 decision trees. At the end of the evolutionary run, a
                 (1+1) Evolution Strategy is used to geometrically
                 optimise the boundaries in the decision space, which
                 are represented by the linear discriminant functions.
                 The evolutionary optimisation concerns maximising the
                 decision-surface margin that is defined to be the
                 smallest distance between the decision-surface and any
                 of the samples. Initial empirical results of the
                 application of our method to a series of datasets from
                 the UCI repository suggest that model generalisation
                 benefits from the margin maximisation, and that the new
                 method is a very competent approach to pattern
                 classification as compared to other learning
  notes =        "Part of \cite{Silva:2011:GP} EuroGP'2011 held in
                 conjunction with EvoCOP2011 EvoBIO2011 and

Genetic Programming entries for Alexandros Agapitos Michael O'Neill Anthony Brabazon Theodoros Theodoridis