abstract = "Transfer learning has been considered a key solution
for the problem of learning when there is a lack of
knowledge in some target domains. Its idea is to
benefit from the learning on different (but related in
some way) domains that have adequate knowledge and
transfer what can improve the learning in the target
domains. Although incompleteness is one of the main
causes of knowledge shortage in many machine learning
real-world tasks, it has received a little effort to be
addressed by transfer learning. In particular, to the
best of our knowledge, there is no single study to
utilize transfer learning for the symbolic regression
task when the underlying data are incomplete. The
current work addresses this point by presenting a
transfer learning method for symbolic regression on
data with high ratios of missing values. A multi-tree
genetic programming algorithm based feature-based
transformation is proposed for transferring data from a
complete source domain to a different, incomplete
target domain. The experimental work has been conducted
on real-world data sets considering different transfer
learning scenarios each is determined based on three
factors: missingness ratio, domain difference, and task
similarity. In most cases, the proposed method achieved
positive transductive transfer learning in both
homogeneous and heterogeneous domains. Moreover, even
with less significant success, the obtained results
show the applicability of the proposed approach for
inductive transfer learning.",