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

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    7 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Pages (from-to)641-651
    Number of pages11
    JournalSmart Innovation, Systems and Technologies
    Volume4
    DOIs
    Publication statusPublished - 2010

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    Keywords

    • Imbalanced data
    • RBF kernel function
    • Rough sets analysis
    • SVM classifier

    ASJC Scopus subject areas

    • Computer Science(all)
    • Decision Sciences(all)

    Cite this

    Watada, J., Lin, L. C., Ding, L., Shapiai, M. I., Chew, L. C., Ibrahim, Z., Jau, L. W., & Khalid, M. (2010). A rough-set-based two-class classifier for large imbalanced dataset. Smart Innovation, Systems and Technologies, 4, 641-651. https://doi.org/10.1007/978-3-642-14616-9_63