Building a memetic algorithm based support vector machine for imbalaced classification

Wu Mingnan, Junzo Watada, Zuwarie Ibrahim, Marzuki Khalid

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Classification analysis is one of core research topics in pattern recognition field. According to the distribution of samples, algorithms like artificial network (ANN) and support vector machine (SVM) have been proposed to perform binary classification. But these traditional classification algorithms hardly work well for imbalanced dataset. In this study, a novel model on the basis of memetic algorithm (MA) and support vector machine (SVM) is proposed to perform the classification for large imbalanced dataset. It is named MSVC (memetic support vector classification) model. Memetic Algorithm is recently proposed and used as a heuristic framework for the large imbalanced classification. Because of the high performance of SVM in balanced binary classification, support vector classification (SVC) is combined with MA to improve the classification accuracy. G-mean is used to check the final result. Compared with some conventional models, the results showed that this model is a proper alternative for imbalanced dataset classification, and it expends the applications of memetic algorithm.

    Original languageEnglish
    Title of host publicationProceedings - 2011 5th International Conference on Genetic and Evolutionary Computing, ICGEC 2011
    Pages389-392
    Number of pages4
    DOIs
    Publication statusPublished - 2011
    Event5th International Conference on Genetic and Evolutionary Computing, ICGEC2011 - Xiamen
    Duration: 2011 Aug 292011 Sep 1

    Other

    Other5th International Conference on Genetic and Evolutionary Computing, ICGEC2011
    CityXiamen
    Period11/8/2911/9/1

    Keywords

    • Classification on imbalanced dataset
    • Memetic algorithm (MA)
    • Memetic support vector classification (MSVC)
    • Support vector machine (SVM)

    ASJC Scopus subject areas

    • Computational Theory and Mathematics
    • Computer Science Applications

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  • Cite this

    Mingnan, W., Watada, J., Ibrahim, Z., & Khalid, M. (2011). Building a memetic algorithm based support vector machine for imbalaced classification. In Proceedings - 2011 5th International Conference on Genetic and Evolutionary Computing, ICGEC 2011 (pp. 389-392). [6042808] https://doi.org/10.1109/ICGEC.2011.95