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The parameters of support vector machine (SVM) are crucial to the model's classification performance. Aiming at the randomicity of selecting the parameters in SVM, this paper presents a method to optimize the parameters of SVM by using genetic algorithm (GA). Using GA's global search to optimize the parameters of SVM and using the chromosome's fitness function to control the type II error rate in...