Developing New Fitness Functions in Genetic Programming for Classification With Unbalanced Data

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

@Article{Bhowan:2012:SMC,
  author =       "Urvesh Bhowan and Mark Johnston and Mengjie Zhang",
  title =        "Developing New Fitness Functions in Genetic
                 Programming for Classification With Unbalanced Data",
  journal =      "IEEE Transactions on Systems, Man, and Cybernetics,
                 Part B: Cybernetics",
  year =         "2012",
  month =        apr,
  volume =       "42",
  number =       "2",
  pages =        "406--421",
  size =         "16 pages",
  abstract =     "Machine learning algorithms such as genetic
                 programming (GP) can evolve biased classifiers when
                 data sets are unbalanced. Data sets are unbalanced when
                 at least one class is represented by only a small
                 number of training examples (called the minority class)
                 while other classes make up the majority. In this
                 scenario, classifiers can have good accuracy on the
                 majority class but very poor accuracy on the minority
                 class(es) due to the influence that the larger majority
                 class has on traditional training criteria in the
                 fitness function. This paper aims to both highlight the
                 limitations of the current GP approaches in this area
                 and develop several new fitness functions for binary
                 classification with unbalanced data. Using a range of
                 real-world classification problems with class
                 imbalance, we empirically show that these new fitness
                 functions evolve classifiers with good performance on
                 both the minority and majority classes. Our approaches
                 use the original unbalanced training data in the GP
                 learning process, without the need to artificially
                 balance the training examples from the two classes
                 (e.g., via sampling).",
  keywords =     "genetic algorithms, genetic programming, GP learning
                 process, biased classifiers, binary classification,
                 class imbalance, data sets, fitness functions, machine
                 learning algorithms, majority class, minority class,
                 training criteria, unbalanced data, unbalanced training
                 data, data handling, learning (artificial
                 intelligence), pattern classification",
  DOI =          "doi:10.1109/TSMCB.2011.2167144",
  ISSN =         "1083-4419",
  notes =        "Also known as \cite{6029340}",
}

Genetic Programming entries for Urvesh Bhowan Mark Johnston Mengjie Zhang

Citations