  In recent years, supervised machine learning models have demonstrated
tremendous success in a variety of application domains. Despite the promising
results, these successful models are data hungry and their performance relies
heavily on the size of training data. However, in many healthcare applications
it is difficult to collect sufficiently large training datasets. Transfer
learning can help overcome this issue by transferring the knowledge from
readily available datasets (source) to a new dataset (target). In this work, we
propose a hybrid instance-based transfer learning method that outperforms a set
of baselines including state-of-the-art instance-based transfer learning
approaches. Our method uses a probabilistic weighting strategy to fuse
information from the source domain to the model learned in the target domain.
Our method is generic, applicable to multiple source domains, and robust with
respect to negative transfer. We demonstrate the effectiveness of our approach
through extensive experiments for two different applications.
