Multiple-source Domain Adaptation in Rule-based Neural Network

摘要

Domain adaptation uses the previously acquired knowledge (source domain) to support predicted tasks in the current domain without sufficient labeled data (target domain). Although many methods have been developed in domain adaptation, one issue hasn’t been solved: how to implement knowledge transfer when more than one source domain is available. In this paper we present a neural network-based method which extracts domain knowledge in the form of rules to facilitate knowledge transfer, merge rules from all source domains and further select related rules for target domain and clip redundant rules. The method presented is validated on datasets that simulate the multi-source scenario and the experimental results verify the superiority of our method in handling multi-source domain adaptation problems.