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Solving the Feature Diversity Problem Based on Multi-Model Scheme

Guanghao Jin1, Na Zhao1, Chunmei Pei1, Hengguang Li2, Qingzeng Song3, Jing Yu1,*

1 School of Telecommunication Engineering, Beijing Polytechnic, Beijing, 100176, China
2 Department of Mathematics, Wayne State University, Detroit, MI, 48202, USA
3 School of Computer Science and Technology, Tiangong University, Tianjin, 300387, China

* Corresponding Author: Jing Yu. Email: email

Journal on Artificial Intelligence 2021, 3(4), 135-143.


Generally, the performance of deep learning models is related to the captured features of training samples. When the training samples belong to different domains, the diverse features may increase the difficulty of training high performance models. In this paper, we built a new framework that generates multiple models on the organized samples to increase the accuracy of classification. Firstly, our framework selects some existing models and trains each of them on organized training sets to get multiple trained models. Secondly, we select some of them based on a validation set. Finally, we use some fusion method on the outputs of the selected models to get more accurate results. The experimental results show that our framework achieved higher accuracy than the existing methods. Our framework can be an option for the deep learning system to increase the classification accuracy.


Cite This Article

G. Jin, N. Zhao, C. Pei, H. Li, Q. Song et al., "Solving the feature diversity problem based on multi-model scheme," Journal on Artificial Intelligence, vol. 3, no.4, pp. 135–143, 2021.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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