Since the original representative methods were implemented with different data sets, it is unfair to directly compare them with our method. Therefore, we implemented the algorithms on our data set, and evaluated the performances of all methods with multiple criteria, such as accuracy (ACC, the percentage of correct predictions), precision (the percentage of true positive instances in all predicted positive predictions), recall (the percentage of predicted true positive predictions in all true positive instances) and area under receiver operating characteristic curve (AUC, comprehensive evaluation of classifier performance, between 0.5 to 1, the larger the better). The result is shown in Table 2.