Multi-Objective Genetic Programming for Classification with Unbalanced Data

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{DBLP:conf/ausai/BhowanZJ09,
  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, http://dblp.uni-trier.de",
  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
                 problems.",
}

Genetic Programming entries for Urvesh Bhowan Mengjie Zhang Mark Johnston

Citations