期刊名称：International Journal of Signal Processing, Image Processing and Pattern Recognition
印刷版ISSN：2005-4254
出版年度：2016
卷号：9
期号：11
页码：147
出版社：SERSC
摘要：The classification accuracy is animportant standard to measure the quality of the classifier. Usually, the classification accuracyis assessed later, not during the classification process. Problems such as classification accuracy dropscannot be timely and effectively found. It is necessary that markingtestsampleswhen estimatingclassification accuracy. It is a problem that we care about that how much is the classification accuracywhen 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 perspectiveswhichbased on the difference between samples. One estimate is based on the difference in samplesdistribution, which is from the Bayesian criterion. Another estimate is based on the difference in each sampleinstance, which is from the K nearest neighbor classification.Classification accuracy is also estimated by using theartificial neural networks, which combinethe characteristics of the above two methods. And results showthe proposed methodshavegood effects.
