Experiments show that this method has small impact on MSE of fault-prone modules and increases the number of defects module prediction accuracy compared to using FSVR to predict the overall test set.

实验结果表明这种方法在对高缺陷数模块预测精度影响不大的情况下，相对于FSVR提高了整体测试集模块缺陷数预测精度。

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The overall predictive accuracy of the classification models using support vector machine were 95.9% for the fathead minnow test set and 95.0% for the honey bee test set.

其中，利用支持向量机分类算法得到的分类模型对呆鲦鱼和蜜蜂毒性测试集的整体预测准确度分别达到95.9%和95.0%。

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For a fixed set of test vectors, the overall test time can be minimized using the scan chain constructed with this method.

对于确定的测试向量集，用该方法构造的扫描链能使电路总的测试时间最少。

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