A SVM-RBF method for solving imbalanced data problem

Lei Ding*, Junzo Watada, Lim Chun Chew, Zuwairie Ibrahim, Lee Wen Jau, Marzuki Khalid


    研究成果: Article査読

    11 被引用数 (Scopus)


    Although knowledge discovery and data mining techniques have successfully resolved a lot of real-world applications, classifying an unbalanced data is still full of challenge. The imbalanced data distribution led to the results of classification emphasis on the majority class. In another word, the accuracy for the minority class is often quite low. Traditional classification methods such as ANN, KNN, cannot solve the problem effectively. How to improve the accuracy of unbalanced data classification has attracted growing attention from both academia and industry. The object of this paper is to build a fused method consisting of data scaling, a re-sampling technique and the SVM-RBF-based method to classify a large unbalanced data set obtained in semiconductor industry. It is a classifier model to predict an output result which will be used for producing health control. The experimental results showed that the classification accuracy of the minority class had a great improvement by using SVM-RBF Model. ICIC International

    ジャーナルICIC Express Letters
    6 B
    出版ステータスPublished - 2010 12月

    ASJC Scopus subject areas

    • コンピュータ サイエンス(全般)
    • 制御およびシステム工学


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