Evolutionary Fuzzy Classifiers for Imbalanced Datasets: An Experimental Comparison

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

@InProceedings{Antonelli:2013:NAFIPS,
  author =       "Michela Antonelli and Pietro Ducange and 
                 Francesco Marcelloni and Armando Segatori",
  title =        "Evolutionary Fuzzy Classifiers for Imbalanced
                 Datasets: An Experimental Comparison",
  booktitle =    "Joint IFSA World Congress and NAFIPS Annual Meeting
                 (IFSA/NAFIPS 2013)",
  year =         "2013",
  month =        jun,
  pages =        "13--18",
  keywords =     "genetic algorithms, genetic programming, database
                 management systems, fuzzy set theory, learning
                 (artificial intelligence), pattern classification,
                 statistical testing, EFC, FRBC, ROC curve, complexity
                 optimisation, embedded rule base generation,
                 evolutionary data base learning, evolutionary fuzzy
                 classifiers, genetic programming algorithm, genetic
                 rule selection, hierarchical fuzzy rule-based
                 classifier, imbalanced datasets, membership function
                 parameters tuning, multiobjective evolutionary learning
                 scheme, nonparametric statistical tests, rule base
                 learning, sensitivity optimisation, specificity
                 optimisation, Accuracy, Biological cells, Complexity
                 theory, Genetics, Input variables, Training, Tuning,
                 Fuzzy Rule-based Classifiers, Genetic and Evolutionary
                 Fuzzy Systems, Imbalanced Datasets",
  DOI =          "doi:10.1109/IFSA-NAFIPS.2013.6608367",
  size =         "6 pages",
  abstract =     "In this paper, we compare three state-of-the-art
                 evolutionary fuzzy classifiers (EFCs) for imbalanced
                 datasets. The first EFC performs an evolutionary data
                 base learning with an embedded rule base generation.
                 The second EFC builds a hierarchical fuzzy rule-based
                 classifier (FRBC): first, a genetic programming
                 algorithm is used to learn the rule base and then a
                 post-process, which includes a genetic rule selection
                 and a membership function parameters tuning, is applied
                 to the generated FRBC. The third EFC is an extension of
                 a multi-objective evolutionary learning scheme we have
                 recently proposed: the rule base and the membership
                 function parameters of a set of FRBCs are concurrently
                 learnt by optimising the sensitivity, the specificity
                 and the complexity. By performing non-parametric
                 statistical tests, we show that, without re-balancing
                 the training set, the third EFC outperforms, in terms
                 of area under the ROC curve, the other comparison
                 approaches.",
  notes =        "Also known as \cite{6608367}",
}

Genetic Programming entries for Michela Antonelli Pietro Ducange Francesco Marcelloni Armando Segatori

Citations