基于ROC曲线的两类分类问题性能评估方法

A New Performance Categories Evaluation Method Based on ROC Curve

为了克服传统分类算法指标存在的不足,即分类准确度、精确度和检测概率等指标对类别先验概率不具有稳健性,采用基于雷达接收机工作特性曲线即ROC曲线的评估方法来评估两类分类算法,通过参数法或非参数法建立ROC曲线,使用几率点欧氏距离、曲线下面积、最佳阈值点等指标对两类算法进行性能评估。实验结果表明,相对于传统算法,基于ROC曲线的评估方法的性能得到了很大的提高,在分类器识别算法性能评估中是一种有效的评估方法。

To overcome the shortage of traditional methods,for example,decision analysis and pattern recognition,precision,the probability of detection and so on,they are not robust to the prior probability of classes,so the ROC curve evaluation method is used in this paper.In order to establish the ROC,uses the parameters method,non-parameters method,the area of the curve method and the best at the threshold as stands.The experimental results show that compared with traditional algorithms,based on ROC curve performance evaluation method has been greatly improved.So it is an effective evaluation measure of the classifier recognition algorithm performance evaluation.