A new semi-supervised inductive transfer learning framework: Co-Transfer
This addresses data scarcity and labeling costs in domain-specific applications like spam detection, but appears incremental as it builds on existing TrAdaBoost methods.
The paper tackles the problem of leveraging labeled and unlabeled data from related source and target domains in scenarios like network intrusion detection, proposing Co-Transfer, a semi-supervised inductive transfer learning framework that generates and refines TrAdaBoost classifiers, with experiments showing it effectively exploits and reuses data.
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.