A hierarchical genetic fuzzy system based on genetic programming for addressing classification with highly imbalanced and borderline data-sets

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Lopez:2013:KS,
  author =       "Victoria Lopez and Alberto Fernandez and 
                 Maria Jose {del Jesus} and Francisco Herrera",
  title =        "A hierarchical genetic fuzzy system based on genetic
                 programming for addressing classification with highly
                 imbalanced and borderline data-sets",
  journal =      "Knowledge-Based Systems",
  volume =       "38",
  pages =        "85--104",
  year =         "2013",
  note =         "Special Issue on Advances in Fuzzy Knowledge Systems:
                 Theory and Application",
  keywords =     "genetic algorithms, genetic programming, Fuzzy rule
                 based classification systems, Hierarchical fuzzy
                 partitions, Genetic rule selection, Tuning, Imbalanced
                 data-sets, Borderline examples",
  ISSN =         "0950-7051",
  DOI =          "doi:10.1016/j.knosys.2012.08.025",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0950705112002596",
  size =         "20 pages",
  abstract =     "Lots of real world applications appear to be a matter
                 of classification with imbalanced data-sets. This
                 problem arises when the number of instances from one
                 class is quite different to the number of instances
                 from the other class. Traditionally, classification
                 algorithms are unable to correctly deal with this issue
                 as they are biased towards the majority class.
                 Therefore, algorithms tend to misclassify the minority
                 class which usually is the most interesting one for the
                 application that is being sorted out. Among the
                 available learning approaches, fuzzy rule-based
                 classification systems have obtained a good behaviour
                 in the scenario of imbalanced data-sets. In this work,
                 we focus on some modifications to further improve the
                 performance of these systems considering the usage of
                 information granulation. Specifically, a positive
                 synergy between data sampling methods and algorithmic
                 modifications is proposed, creating a genetic
                 programming approach that uses linguistic variables in
                 a hierarchical way. These linguistic variables are
                 adapted to the context of the problem with a genetic
                 process that combines rule selection with the
                 adjustment of the lateral position of the labels based
                 on the 2-tuples linguistic model. An experimental study
                 is carried out over highly imbalanced and borderline
                 imbalanced data-sets which is completed by a
                 statistical comparative analysis. The results obtained
                 show that the proposed model outperforms several fuzzy
                 rule based classification systems, including a
                 hierarchical approach and presents a better behavior
                 than the C4.5 decision tree.",
}

Genetic Programming entries for Victoria Lopez Morales Alberto Fernandez Maria Jose del Jesus Francisco Herrera

Citations