  Accurately credit default prediction faces challenges due to imbalanced data
and low correlation between features and labels. Existing default prediction
studies on the basis of gradient boosting decision trees (GBDT), deep learning
techniques, and feature selection strategies can have varying degrees of
success depending on the specific task. Motivated by this, we propose
Tab-Attention, a novel self-attention-based stacked generalization method for
credit default prediction. This approach ensembles the potential proprietary
knowledge contributions from multi-view feature spaces, to cope with low
feature correlation and imbalance. We organize multi-view feature spaces
according to the latent linear or nonlinear strengths between features and
labels. Meanwhile, the f1 score assists the model in imbalance training to find
the optimal state for identifying minority default samples. Our Tab-Attention
achieves superior Recall_1 and f1_1 of default intention recognition than
existing GBDT-based models and advanced deep learning by about 32.92% and
16.05% on average, respectively, while maintaining outstanding overall
performance and prediction performance for non-default samples. The proposed
method could ensemble essential knowledge through the self-attention mechanism,
which is of great significance for a more robust future prediction system.
