A FUZZY DOMAIN ADAPTATION METHOD BASED ON SELF-CONSTRUCTING FUZZY NEURAL NETWORK

Domain adaptation addresses the problem of how to utilize a model trained in the source domain to make predictions for target domain when the distribution between two domains differs substantially and labeled data in target domain is costly to collect for retraining. Existed studies are incapable to handle the issue of information granularity, in this paper, we propose a new fuzzy domain adaptation method based on self-constructing fuzzy neural network. This approach models the transferred knowledge supporting the development of the current models granularly in the form of fuzzy sets and adapts the knowledge using fuzzy similarity measure to reduce prediction error in the target domain.