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- W2379876751 abstract "Support Vector Machine is a quite efficient classification technique developed on statistical learning theory.However,when the two-class problem samples are very unbalanced,SVM has a poor performance.To significantly improve the classification performance of imbalanced datasets,the nature characteristics of Sparse Least Squares SVM is analyzed and a kind of algorithm for the unbalanced samples is proposed in this paper.The experiments on the UCI database are done with this algorithm.Experimental results indicate that this method significantly improves the classification accuracy of SVM for the unbalanced samples.The speed of classification is much faster than that of conventional SVM in the condition that the correct rate does not decline,especially in the case of large number of support vectors." @default.
- W2379876751 created "2016-06-24" @default.
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- W2379876751 date "2008-01-01" @default.
- W2379876751 modified "2023-09-27" @default.
- W2379876751 title "Research on classification of imbalanced data based on Sparse Least Squares Support Vector Machines" @default.
- W2379876751 hasPublicationYear "2008" @default.
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