earticle

영어

The classification accuracy is an important standard to measure the quality of the classifier. Usually, the classification accuracy is assessed later, not during the classification process. Problems such as classification accuracy drops cannot be timely and effectively found. It is necessary that marking test samples when estimating classification accuracy. It is a problem that we care about that how much is the classification accuracy when a group of new samples obtained. The problem must be concerned when using and improving the classifier in the case of growing data. To solve this problem, this paper put forward different estimates from different perspectives which based on the difference between samples. One estimate is based on the difference in samples distribution, which is from the Bayesian criterion. Another estimate is based on the difference in each sample instance, which is from the K nearest neighbor classification. Classification accuracy is also estimated by using the artificial neural networks, which combine the characteristics of the above two methods. And results show the proposed methods have good effects.