  Current Instance Transfer Learning (ITL) methodologies use domain adaptation
and sub-space transformation to achieve successful transfer learning. However,
these methodologies, in their processes, sometimes overfit on the target
dataset or suffer from negative transfer if the test dataset has a high
variance. Boosting methodologies have been shown to reduce the risk of
overfitting by iteratively re-weighing instances with high-residual. However,
this balance is usually achieved with parameter optimization, as well as
reducing the skewness in weights produced due to the size of the source
dataset. While the former can be achieved, the latter is more challenging and
can lead to negative transfer. We introduce a simpler and more robust fix to
this problem by building upon the popular boosting ITL regression methodology,
two-stage TrAdaBoost.R2. Our methodology,~\us{}, is a boosting and
random-forest based ensemble methodology that utilizes importance sampling to
reduce the skewness due to the source dataset. We show that~\us{}~performs
better than competitive transfer learning methodologies $63\%$ of the time. It
also displays consistency in its performance over diverse datasets with varying
complexities, as opposed to the sporadic results observed for other transfer
learning methodologies.
