Evolutionary Fuzzy Classifiers for Imbalanced Datasets: An Experimental Comparison

Created by W.Langdon from gp-bibliography.bib Revision:1.8187

@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 = "24-28 " # jun,
• pages = "13--18",
• address = "Edmonton, Canada",
• 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",
• isbn13 = "978-1-4799-0348-1",
• 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