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