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- W2348974064 abstract "The proximal support vector machine is a very fast classifier, compared to the standard support vector machine. But when it is applied to the problem with two classes on unbalanced dataset, due to the significant difference in the cardinality of the two classes, it tends to fit better the class with more data points and underestimates the overall error of the class with fewer data points. This leads to the poor classification performance. In order to surmount the difficulty, we propose the algorithm of improving the PSVM, which adaptively punishes or encourages the misclassification error according to the sample number misclassified and results in a better classifier. We conduct experiments with the two-dimension normal distribution sample point, and the experimental results demonstrate the validation of the algorithm." @default.
- W2348974064 created "2016-06-24" @default.
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- W2348974064 date "2004-01-01" @default.
- W2348974064 modified "2023-09-23" @default.
- W2348974064 title "DFP-PSVM Classifier: A Method of Improving the Accuracy of PSVM Classifier on the Unbalanced Datasets" @default.
- W2348974064 hasPublicationYear "2004" @default.
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