A Hybrid Approach to the Problem of Class Imbalance

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Jeannie Fitzgerald and Conor Ryan",
  title =        "A Hybrid Approach to the Problem of Class Imbalance",
  booktitle =    "19th International Conference on Soft Computing,
                 MENDEL 2013",
  year =         "2013",
  editor =       "Radomil Matousek",
  pages =        "129--137",
  address =      "Brno, Czech Republic",
  month =        jun # " 26-28, Brno",
  organisation = "Brno University of Technology",
  keywords =     "genetic algorithms, genetic programming, class
                 imbalance, Binary Classification, Class Imbalance
                 Problem, Over Sampling, Under Sampling",
  isbn13 =       "978-80-214-4755-4",
  URL =          "https://www.researchgate.net/publication/264670826_A_Hybrid_Approach_to_the_Problem_of_Class_Imbalance?ev=prf_pub",
  size =         "8 pages",
  abstract =     "In Machine Learning classification tasks, the class
                 imbalance problem is an important one which has
                 received a lot of attention in the last few years. In
                 binary classification, class imbalance occurs when
                 there are significantly fewer examples of one class
                 than the other. A variety of strategies have been
                 applied to the problem with varying degrees of success.
                 Typically previous approaches have involved attacking
                 the problem either algorithmically or by manipulating
                 the data in order to mitigate the imbalance. We propose
                 a hybrid approach which combines Proportional
                 Individualised Random Sampling(PIRS) with two different
                 fitness functions designed to improve performance on
                 imbalanced classification problems in Genetic
                 Programming. We investigate the efficacy of the
                 proposed methods together with that of five different
                 algorithmic GP solutions, two of which are taken from
                 the recent literature. We conclude that the PIRS
                 approach combined with either average accuracy or
                 Matthews Correlation Coefficient, delivers superior
                 results in terms of AUC score when applied to either
                 balanced or imbalanced datasets.",
  notes =        "http://www.mendel-conference.org/

Genetic Programming entries for Jeannie Fitzgerald Conor Ryan