基于AUC方法评估多类别贝叶斯分类器的性能

Evaluating performance of multiple Bayes classifier based on AUC method

分类器评估一般采用准确性评估。理论证明,基于AUC方法评估分类器优于准确性评估方法,但该方法局限于二类分类问题。提出一种将二类分类问题推广到多类分类问题的新方法,用纠错输出码转换得到转换矩阵,通过转换矩阵把多类分类问题转换成二类分类问题,计算二类分类的平均值来评估分类器的性能。新方法在MBNC实验平台下编程实现,并评估贝叶斯分类器的性能,实验结果表明,这种方法是有效的。

The evaluation of classifiers has been an important study in data mining and machine learning field. AUC （area under the receiver operating characteristic curve） is determined as a better way to evaluate classifiers than predictive accuracy. However, AUC only is used for two classes to date. A new method is referred. A conversion matrix is received by using error correcting output codes. Based on conversation matrix, multiple-classifier is turned into two-classifier. Computing the AUC value for each two-classifier and average all of the AUCs of two-classifier. The average of AUC value is used as a criterion for evaluating the performance of classifiers. Making experiment in MBNC experiment platform, the results show that the new method is effective.