  In many practical data mining scenarios, such as network intrusion detection,
Twitter spam detection, and computer-aided diagnosis, a source domain that is
different from but related to a target domain is very common. In addition, a
large amount of unlabeled data is available in both source and target domains,
but labeling each of them is difficult, expensive, time-consuming, and sometime
unnecessary. Therefore, it is very important and worthwhile to fully explore
the labeled and unlabeled data in source and target domains to settle the task
in target domain. In this paper, a new semi-supervised inductive transfer
learning framework, named Co-Transfer is proposed. Co-Transfer first generates
three TrAdaBoost classifiers for transfer learning from the source domain to
the target domain, and meanwhile another three TrAdaBoost classifiers are
generated for transfer learning from the target domain to the source domain,
using bootstraped samples from the original labeled data. In each round of
co-transfer, each group of TrAdaBoost classifiers are refined using the
carefully labeled data. Finally, the group of TrAdaBoost classifiers learned to
transfer from the source domain to the target domain produce the final
hypothesis. Experiments results illustrate Co-Transfer can effectively exploit
and reuse the labeled and unlabeled data in source and target domains.
