Integration of deep learning model and feature selection for multi-label classification

Document Type : Research Paper


1 Department of IT and Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran

2 Assistant Professor, Department of IT and Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran


Multi-label data classification differs from traditional single-label data classification, in which each input sample participated with just one class tag. As a result of the presence of multiple class tags, the learning process is affected, and single-label classification can no longer be used. Methods for changing this problem have been developed. By using these methods, one can run the usual classifier classes on the data. Multi-label classification algorithms are used in a variety of fields, including text classification and semantic image annotation. A novel multi-label classification method based on deep learning and feature selection is presented in this paper with specific meta-label-specific features. The results of experiments on different multi-label datasets demonstrate that the proposed method is more efficient than previous methods.


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Volume 13, Issue 1
March 2022
Pages 2871-2883
  • Receive Date: 10 October 2021
  • Revise Date: 28 November 2021
  • Accept Date: 26 December 2021