Application of classifier based on DRNG algorithm

With the advent of the era of big data, semi-supervised learning algorithms are becoming more and more popular and widely used. Self-training is the most widely used semi-supervised learning framework. The performance of the classifier obtained by self-training mainly depends on the selection of high-confidence samples during the self-training process. To study the effect of the base classifier on the performance of the DRNG algorithm, we use KNN, decision tree, and SVM base classifier to conduct experiments on four medical images. The experimental results show on the most datasets, the classification performance of the DRNG algorithm on the KNN (K=1) base classifier is higher than the other three base classifiers. The DRNG algorithm matches the KNN(K=1) base classifier better.