*4.2. Baselines*

Other than our proposed model, four classical models in machine learning are given for comparison. The basic parameters setting for baseline methods are summarized in Table 2.


The general process of the experiments for all methods is as follows:

At first, we divide one dataset into two parts, one for training and the other for e ffect evaluation. The ratio of these two parts is called the training ratio. It is worth noting that positive samples and negative samples are divided separately, so the ratio of positive and negative samples in the training dataset is the same as that in the test dataset. At each training ratio, we implement ten experiments. The division of the training dataset and the test dataset is random and independent in each experiment. At last, we use the average result of these ten experiments to represent the final results.


