To overcome these limitations, we propose a new self-training 26 method for effectively augmenting negative samples. This method was applied to a graph-based CPI prediction model and successfully improved the model performance, including generalizability. Concretely, the CPI prediction model trained with our approach showed higher robustness on the external dataset than other methods addressing class imbalance. Furthermore, examination of the parameters in our approach revealed that the pseudolabeled samples predicted with "near-boundary" scores were more beneficial for model generalizability than the samples readily classified as negative.