Community-Based Hierarchical Positive-Unlabeled (PU) Model Fusion for
  Chronic Disease Prediction
Yang Wu Xurui Li Xuhong Zhang Yangyang Kang Changlong Sun Xiaozhong Liu
Positive-Unlabeled (PU) Learning is a challenge presented by binary
classification problems where there is an abundance of unlabeled data along
with a small number of positive data instances, which can be used to address
chronic disease screening problem. State-of-the-art PU learning methods have
resulted in the development of various risk estimators, yet they neglect the
differences among distinct populations. To address this issue, we present a
novel Positive-Unlabeled Learning Tree (PUtree) algorithm. PUtree is designed
to take into account communities such as different age or income brackets, in
tasks of chronic disease prediction. We propose a novel approach for binary
decision-making, which hierarchically builds community-based PU models and then
aggregates their deliverables. Our method can explicate each PU model on the
tree for the optimized non-leaf PU node splitting. Furthermore, a mask-recovery
data augmentation strategy enables sufficient training of the model in
individual communities. Additionally, the proposed approach includes an
adversarial PU risk estimator to capture hierarchical PU-relationships, and a
model fusion network that integrates data from each tree path, resulting in
robust binary classification results. We demonstrate the superior performance
of PUtree as well as its variants on two benchmarks and a new
diabetes-prediction dataset.