The Classification of Multi-Domain Samples Based on the Cooperation of Multiple Models - Qingzeng Song; Junting Xu; Lei Ma; Ping Yang; Guanghao Jin (Complexity)
This article proposed a novel classification framework that can classify the samples of multiple domains based on the outputs of multiple models. Different from the existing methods that train single model on all domains, our framework trains multiple models on each domain. On a testing sample, the outputs of all trained models are used to predict the domain of this sample. Then, this sample is classified by the output of models that belong to the predicted domain. Experiments show that our framework achieved higher accuracy than the existing methods. Furthermore, our framework achieves good scalability on multiple domains.