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- W2386896262 abstract "The classification result of classical support vector machine(SVM) algorithm in the case of unbalanced data set is not satisfactory.In order to improve the SVM algorithm's classification performance under unbalanced data set,a novel algorithm based on random under-sampling and synthetic minority over-sample technique(SMOTE) is presented in this paper.The Random under-sampling is firstly applied to under-sample the majority class instances for removal of a large number of overlapping samples of redundant and noise samples,consequently making reservations for the majority class instances with more useful information;and then SMOTE is used to over-sample the minority class boundary instances,such that can be more conducive to generate the effective classification interface of SVM algorithm.The experimental results on UCI datasets compared with other sampling algorithms show that the proposed method can not only improve classification performance of SVM in the minority class data,but also increase the overall classification performance." @default.
- W2386896262 created "2016-06-24" @default.
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- W2386896262 date "2012-01-01" @default.
- W2386896262 modified "2023-09-26" @default.
- W2386896262 title "The SVM classifier for unbalanced data based on combination of RU-Undersample and SMOTE" @default.
- W2386896262 hasPublicationYear "2012" @default.
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