Multisource Domain Adaptation With Interval-Valued Target Data via Fuzzy Neural Networks
Guangzhi Ma, Jie Lü, Guangquan Zhang
Abstract
Multi-source domain adaptation (MSDA) refers to the task of adapting a model from multiple source domains to a target domain that shares a different distribution with all source domains. However, most existing MSDA works focus on crispvalued data, while such data may not be available in some realworld scenarios. For example, data extracted by many measuring devices are not exact numbers but rather intervals. In this paper, a highly challenging problem called MSDA with interval-valued target data is presented. The objective is to learn a new model for interval-valued target data by leveraging knowledge from source models trained on multiple crisp-valued source data. First, a theoretical analysis is given to inform the appropriate combination of multi-source models. Then, we propose a new neural network model based on a fuzzy transformation function and fuzzy distances to address the proposed problem. The fuzzy transformation function is applied to extract valuable crisp-valued information from interval-valued target data, while fuzzy distances are designed to guide the fusion of multiple source models. Experiments on both synthetic and real-world datasets verify the superiority of our proposed MSDA method for classification task. Furthermore, the results of the ablation study and parameter sensitivity analysis illustrate the rationality of the proposed fuzzy distance-based model.