Designing fuzzy imbalanced classifier based on the subtractive clustering and Genetic Programming

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@InProceedings{Mahdizadeh:2013:IFSC,
  author =       "Mahboubeh Mahdizadeh and Mahdi Eftekhari",
  booktitle =    "13th Iranian Conference on Fuzzy Systems (IFSC 2013)",
  title =        "Designing fuzzy imbalanced classifier based on the
                 subtractive clustering and Genetic Programming",
  year =         "2013",
  month =        "27-29 " # aug,
  keywords =     "genetic algorithms, genetic programming, Fuzzy
                 Inference System, Differential Evolution, Subtractive
                 clustering, Multi-Gene Genetic programming",
  DOI =          "doi:10.1109/IFSC.2013.6675611",
  abstract =     "In this paper, a design methodology is proposed for
                 generating a fuzzy rule-based classifier for imbalanced
                 datasets. The classifier is based on Sugeno-type Fuzzy
                 Inference System. It is generated by using of
                 subtractive clustering and Multi-Gene Genetic
                 Programming to obtain fuzzy rules. The subtractive
                 clustering is used for producing the antecedents of
                 rules and Multi-Gene Genetic Programming is employed
                 for generating the functions in the consequence parts
                 of rules. Feature selection is used as an important
                 pre-processing step for dimension reduction.
                 Experiments are performed with 8 datasets from KEEL.
                 The comparison results reveal that the proposed
                 classifier outperforms the other methods.",
  notes =        "Also known as \cite{6675611}",
}

Genetic Programming entries for Mahboubeh Mahdizadeh Mehdi Eftekhari

Citations