【摘要】 在数据挖掘领域中,不同分类器建立的模型性能不尽相同。对分类器性能的评价是选择优秀分类器的基础。为了更好地对分类器性能进行评估,文中对分类器性能评价标准进行了研究。分析了传统分类器性能评价标准在应用时存在的一些问题,重点介绍了ROC曲线(the Receiver Operating Characteristic curve)和AUC(the area under the ROC curve)评价方法,并剖析了它们的优缺点。对比分析表明,ROC曲线和AUC方法虽然存在着一定的不足,但是在分类器性能评价中所表现出的诱人性质使其必定具有广阔的应用前景。更多还原

【Abstract】 The performances of classification models are different in data mining.How to select a good classifier is based on the evaluation of classifiers performances.Researches the measure criteria of classifiers performances in order to estimate classifiers effectively.The problems about the traditional measure criteria of classifiers performances are analyzed.ROC and AUC are introduced emphatically,and their virtue and shortcoming are anatomized.From the comparison and analysis,it shows that ROC and AUC are so attractive that they will be applied extensively,in spite of their shortcomings.更多还原