Abstract
In peer-to-peer (P2P) social lending, it is important to predict the repayment of borrowers. P2P lending data are generated in real-time, but most of them are pending to decide the repayment because the deadline is not yet expired. Adding the unexpired data with appropriate labels into the training set could improve the performance of a prediction model, but the pseudo-labels cannot be certainly precise. In this paper, we propose an ensemble classifier composed of diverse convolutional neural networks (CNNs) of GoogLeNet, ResNet and DenseNet for the repayment prediction in social lending with the pseudo-labels approximated by an uncertainty handling scheme. The additional data labeled by Dempster-Shafer fusion of two semi-supervised learning methods boost up training of various models of CNNs, which are combined by weighted voting. A diversity measure is applied to constructing a pool of different models of CNNs that extract the effective features in the social lending data with labeling noise and predict the borrower's loan status. The experiment with the real dataset of 855,502 cases from Lending Club confirms that the diverse ensemble combined with weighted voting achieves the highest performance and outperforms conventional methods.