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- W2064742018 abstract "As a supervised classification algorithm, Support Vector Machine (SVM) has an excellent ability in solving small samples, nonlinear and high dimensional classification problems. However, SVM is inefficient for imbalanced data sets classification. Therefore, a cost sensitive SVM (CSSVM) should be designed for imbalanced data sets classification. This paper proposes a method which constructed CSSVM based on information entropy, and in this method the information entropies of different classes of data set are used to determine the values of penalty factor of CSSVM." @default.
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- W2064742018 date "2014-07-01" @default.
- W2064742018 modified "2023-09-22" @default.
- W2064742018 title "Imbalanced Data Classification Using Cost-Sensitive Support Vector Machine Based on Information Entropy" @default.
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- W2064742018 doi "https://doi.org/10.4028/www.scientific.net/amr.989-994.1756" @default.
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