论文标题
通过机器学习算法优化测试集优化
Test Set Optimization by Machine Learning Algorithms
论文作者
论文摘要
诊断结果高度取决于测试集的量。为了得出最有效的测试集，我们提出了几种基于机器学习的方法，以预测产生相对准确诊断的测试数据的最低量。通过从故障电路中收集输出，生成了特征矩阵和标签向量，其中涉及测试终止点的推理信息。因此，我们开发了一个预测模型，以符合数据并确定何时终止测试。所考虑的方法包括套索和支持向量机（SVM），其中目标（标签）和预测因子（特征矩阵）之间的关系被认为是Lasso中的线性和SVM中的非线性。数值结果表明，SVM达到90.4％的诊断精度，同时将测试量减少了35.24％。
Diagnosis results are highly dependent on the volume of test set. To derive the most efficient test set, we propose several machine learning based methods to predict the minimum amount of test data that produces relatively accurate diagnosis. By collecting outputs from failing circuits, the feature matrix and label vector are generated, which involves the inference information of the test termination point. Thus we develop a prediction model to fit the data and determine when to terminate testing. The considered methods include LASSO and Support Vector Machine(SVM) where the relationship between goals(label) and predictors(feature matrix) are considered to be linear in LASSO and nonlinear in SVM. Numerical results show that SVM reaches a diagnosis accuracy of 90.4% while deducting the volume of test set by 35.24%.
