KNN classification algorithm based on rule of weak learning on small sample sets
基于小样本集弱学习规则的KNN分类算法

KNN and its improved algorithms identify the class labels of the unlabel datasets by using the label datasets , if the data objects in are very little, and this will influence the accuracy of classification. Improving the accuracy of classification is the goal of KNN classification algorithm based on the rule of weak learning on small sample sets, which learns the label information of objects in based on firstly, selects some data objects in and labels them by using the learned label information, and then adds those data objects into , finally labels the objects in based on the expanded label datasets . The accuracy of the presented method is demonstrated with standard datasets, and obtains a satisfying result.