Genetic Cooperative-Competitive Fuzzy Rule Based Learning Method using Genetic Programming for Highly Imbalanced Data-Sets

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

@InProceedings{conf/eusflat/FernandezBJH09,
  title =        "Genetic Cooperative-Competitive Fuzzy Rule Based
                 Learning Method using Genetic Programming for Highly
                 Imbalanced Data-Sets",
  author =       "Alberto Fernandez and Francisco Jose Berlanga and 
                 Maria Jose {del Jesus} and Francisco Herrera",
  booktitle =    "Proceedings of the Joint 2009 International Fuzzy
                 Systems Association World Congress and 2009 European
                 Society of Fuzzy Logic and Technology Conference",
  year =         "2009",
  editor =       "Jo{\~a}o Paulo Carvalho and Didier Dubois and 
                 Uzay Kaymak and Jo{\~a}o Miguel da Costa Sousa",
  pages =        "42--47",
  address =      "Lisbon, Portugal",
  month =        jul # " 20-24",
  keywords =     "genetic algorithms, genetic programming, Fuzzy
                 Rule-Based Classification Systems, Genetic Fuzzy
                 Systems, imbalanced Data-Sets, Interpretability",
  isbn13 =       "978-989-95079-6-8",
  URL =          "http://www.eusflat.org/publications/proceedings/IFSA-EUSFLAT_2009/pdf/tema_0042.pdf",
  bibdate =      "2009-12-16",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/eusflat/eusflat2009.html#FernandezBJH09",
  abstract =     "Classification in imbalanced domains is an important
                 problem in Data Mining. We refer to imbalanced
                 classification when data presents many examples from
                 one class and few from the other class, and the less
                 representative class is the one which has more interest
                 from the point of view of the learning task. The aim of
                 this work is to study the behaviour of the GP-COACH
                 algorithm in the scenario of data-sets with high
                 imbalance, analysing both the performance and the
                 interpretability of the obtained fuzzy models. To
                 develop the experimental study we will compare this
                 approach with a well-known fuzzy rule learning
                 algorithm, the Chi et al.'s method, and an algorithm of
                 reference in the field of imbalanced data-sets, the
                 C4.5 decision tree.",
  notes =        "1.Department of Computer Science and Artificial
                 Intelligence, University of Granada Granada, Spain
                 2.Department of Computer Science and Systems
                 Engineering, University of Zaragoza Zaragoza, Spain
                 3.Department of Computer Science, University of Jaen
                 Spain",
}

Genetic Programming entries for Alberto Fernandez Francisco Jose Berlanga Maria Jose del Jesus Francisco Herrera

Citations