Multi-Objective Genetic Programming for Classification with Unbalanced Data

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

  author =       "Urvesh Bhowan and Mengjie Zhang and Mark Johnston",
  title =        "Multi-Objective Genetic Programming for Classification
                 with Unbalanced Data",
  booktitle =    "Proceedings of the 22nd Australasian Joint Conference
                 on Artificial Intelligence (AI'09)",
  year =         "2009",
  editor =       "Ann E. Nicholson and Xiaodong Li",
  volume =       "5866",
  series =       "Lecture Notes in Computer Science",
  pages =        "370--380",
  bibsource =    "DBLP,",
  address =      "Melbourne, Australia",
  month =        dec # " 1-4",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-10438-1",
  DOI =          "doi:10.1007/978-3-642-10439-8_38",
  abstract =     "Existing learning and search algorithms can suffer a
                 learning bias when dealing with unbalanced data sets.
                 This paper proposes a Multi-Objective Genetic
                 Programming (MOGP) approach to evolve a Pareto front of
                 classifiers along the optimal trade-off surface
                 representing minority and majority class accuracy for
                 binary class imbalance problems. A major advantage of
                 the MOGP approach is that by explicitly incorporating
                 the learning bias into the search algorithm, a good set
                 of well-performing classifiers can be evolved in a
                 single experiment while canonical (single-solution)
                 Genetic Programming (GP) requires some objective
                 preference be a priori built into a fitness function.
                 Our results show that a diverse set of solutions was
                 found along the Pareto front which performed as well or
                 better than canonical GP on four class imbalance

Genetic Programming entries for Urvesh Bhowan Mengjie Zhang Mark Johnston