A utility-driven approach to instance-based transfer learning for relational domains - Pereira, Cainã F.; Menasché, Daniel S.; Zaverucha, Gerson; Paes, Aline; Barbosa, Valmir C. (Machine learning, 2025)
Statistical relational learning involves exploring a complex search space of objects, their relationships, and probability parameters to find an optimal model. To reduce search complexity, previous work has explored taking advantage of a learned model in a source domain and transfer it to a target domain. However, these models are not always available and imperfect learning in the source domain can hinder the performance in the target domain. This paper proposes to leverage the instances of a source domain instead of its learned model. A simple solution, such as concatenating instances from both domains, is likely ineffective due to the potential negative impact of irrelevant or poor-quality instances. We address this by framing instance selection as a task of fair resource allocation, where utilities are parameterized to capture the relevance of each instance. We introduce a method called UTIL-BRDN, which applies this utility-driven approach to Boosted Relational Dependency Networks (RDN-Boost). Our experimental results show that UTIL-BRDN effectively transfers knowledge by reusing instances from other domains and is robust against negative transfer. Our contributions include introducing instance-based transfer learning to statistical relational learning, developing a utility-driven approach to instance selection, extending RDN-Boost to handle multiple domains and utilities, and conducting an extensive empirical evaluation of the proposed method.