A rough-set-based two-class classifier for large imbalanced dataset

Junzo Watada, Lee Chuan Lin, Lei Ding, Mohd Ibrahim Shapiai, Lim Chun Chew, Zuwairie Ibrahim, Lee Wen Jau, Marzuki Khalid

    研究成果: Article査読

    8 被引用数 (Scopus)


    The objective of this paper is to provide a rouch-set-based two-class classifier approach to classifying samples in large and imbalanced dataset. A database has plenty of hidden knowledge, which can be used in decision making to support commerce, research and other activities. Prediction is another form of expanding data analysis. It enables us to establish a data model using existing data and to predict the trend of data in future. In this paper, a method consists of data scaling, rough sets analysis and support vector machine with radial basis function (SVM-RBF), which is used to classify a large and imbalanced data set obtained in semiconductor industry.

    ジャーナルSmart Innovation, Systems and Technologies
    出版ステータスPublished - 2010

    ASJC Scopus subject areas

    • コンピュータ サイエンス(全般)
    • 決定科学(全般)


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