Evaluating performance of multiple Bayes classifier based on AUC method
Chen Li · Jisuanji gongcheng yu sheji · 2007
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.