A New Multi-Target Domain Adaptation Method Based on Evidence Theory for Distribution Inconsistent Data Classification

Abstract

In the context of distribution inconsistent data classification, addressing distribution shift is crucial, typically accomplished through domain adaptation (DA) techniques. Once distributions are aligned between the source and target domains, the problem transforms into a conventional recognition task. This article introduces a new method called multitarget DA based on Evidence Theory (MET). For a given target domain, a random merger with other target domains is performed, generating distinct new target domains. Domain-invariant features corresponding to each new target domain are learned by minimizing distribution discrepancies separately between the source and different new target domains. The merging of target domains alters the distribution of the new target domain, leading to variations in the retained information within the learned domain-invariant features. For a query pattern in this target domain, multiple soft classification results (CCR) are obtained after aligning the distributions of the source and different new target domains. These soft CCR complement each other, and evidence theory is employed as a tool to represent and combine uncertain information, fusing these results. The weights for this fusion are automatically learned by minimizing the mean squared error between the combined results and the ground truth on labeled source domain data. The final class decision is determined through the weighted evidential combination of multiple pieces of soft CCR. MET is assessed on several datasets (i.e., Office+Caltech-10, VLSC, and V-RSIR) and compared to various advanced DA methods (e.g., GNN, MT, PAL, PTD, and so on) to validate its effectiveness. The experimental results demonstrate that MET usually can obtain a higher classification performance (i.e., the accuracy can be improved by 2% compared to many methods in most cases).