Meistrell M L
Program in Medical Information Science, Dartmouth Medical School, Hanover, NH 03756.
Comput Methods Programs Biomed. 1990 May;32(1):73-80. doi: 10.1016/0169-2607(90)90087-p.
A need exists for an unbiased measure of the accuracy of feed-forward neural networks used for classification. Receiver operating characteristic (ROC) analysis is suited for this measure, and has been used to assess the performance of several different network weights. The area under an ROC and its standard error were used to compare different network weight sets, and to follow the performance of a network during the course of training. The ROC is not sensitive to the prior probabilities of examples in the testing set nor to the system's decision bias. The area under an ROC curve is a readily understood measure, and should be used to evaluate neural networks and to report results of learning experiments. Examples are provided from experiments with data from the biotechnology domain.
需要一种用于评估前馈神经网络分类准确性的无偏测量方法。接收者操作特征（ROC）分析适用于此测量，并且已用于评估几种不同网络权重的性能。ROC曲线下的面积及其标准误差用于比较不同的网络权重集，并跟踪训练过程中网络的性能。ROC对测试集中示例的先验概率和系统的决策偏差不敏感。ROC曲线下的面积是一种易于理解的测量方法，应用于评估神经网络并报告学习实验的结果。文中提供了来自生物技术领域数据实验的示例。
