Open Access
ARTICLE
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:
Journal on Artificial Intelligence 2021, 3(4), 135-143. https://doi.org/10.32604/jai.2021.027154
Received 11 January 2022; Accepted 21 January 2022; Issue published 07 February 2022
Abstract
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.
Keywords
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.