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- W2355756195 abstract "To improve the performance of traditional support vector machine (SVM) on the dataset with unbalanced class distribution, an improved SVM was presented. Genetic algorithm-SVM (GA-SVM) was constructed by combining the genetic algorithm and the simple support vector machine. The parameters of SVM were coded into chromosomes with Gray coding strategy. After optimally searching in the parameter space, the outcome was used as the final parameters of SVM. GA-SVM was tested on two far different datasets from machine learning repository. Compared with the standard SVM with cross validation strategy, the simulation results indicate the superiority of GA-SVM on the dataset with unbalanced classes. GA-SVM can gain higher classification accurate and faster learning speed, and work well with faster learning speed on the perfectly constructed dataset." @default.
- W2355756195 created "2016-06-24" @default.
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- W2355756195 date "2004-01-01" @default.
- W2355756195 modified "2023-09-26" @default.
- W2355756195 title "Improved support vector machine for multi-class classification problems" @default.
- W2355756195 hasPublicationYear "2004" @default.
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