The Classification of Multi-Domain Samples Based on the Cooperation of Multiple Models. (25th September 2022)

• Record Type: Journal Article
• Authors: Song, Qingzeng; Xu, Junting; Ma, Lei; Yang, Ping; Jin, Guanghao
• Other Names: Volchenkov Dimitri Academic Editor.
• Is Part Of: Complexity. Volume 2022(2022)
• Publication Date: 2022-09-25
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2022/5578043
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : 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.