Since the lack of lesion labels and unbalanced data distribution in datasets lead to the problem that the supervised classification model can not effectively classify the lesions in the traditional Diabetic Retinopathy (DR) grading system, a Cost-Sensitive based Semi-supervised Bagging (CS-SemiBagging) algorithm for DR classification was proposed. Firstly, retinal vessels were removed from a fundus image, and then the suspicious red lesions (MicroAneurysms (MAs) and HEMorrhages (HEMs)) were detected on the image without vessels. Secondly, a 22-dimensional feature based on color, shape and texture was extracted to describe each candidate lesion region. Thirdly, a CS-SemiBagging model was constructed for the classification of MAs and HEMs. Finally, the severity of DR was graded into four levels based on the numbers of different lesions. The proposed method was evaluated on the publicly available MESSIDOR database. It achieved an average accuracy of 90.2%, which was 4.9 percentage points higher than that of classical semi-supervised learning method based on Co-training. The CS-SemiBagging algorithm can effectively classify DR without label information of the suspicious lesions, so as to avoid the time-consuming effort of labeling the lesions by specialists and the bad influence of unbalanced samples on the classification.