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

Ebrahimi, Hossein; Majidzadeh, Kambiz; Soleimanian Gharehchopogh, Farhad

doi:10.22075/ijnaa.2021.25379.2998

اداره چاپ و انتشارات دانشگاه سمنان

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	Integration of deep learning model and feature selection for multi-label classification
International Journal of Nonlinear Analysis and Applications
مقاله 232، دوره 13، شماره 1، خرداد 2022، صفحه 2871-2883 اصل مقاله (1.05 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22075/ijnaa.2021.25379.2998
نویسندگان
Hossein Ebrahimi¹؛ Kambiz Majidzadeh^* ²؛ Farhad Soleimanian Gharehchopogh²
¹Department of IT and Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran
²Assistant Professor, Department of IT and Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran
تاریخ دریافت: 18 مهر 1400، تاریخ بازنگری: 07 آذر 1400، تاریخ پذیرش: 05 دی 1400
چکیده
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.
کلیدواژه‌ها
Machine Learning؛ Classification؛ Multi-Label؛ Meta-Label-Specific Features؛ Deep Learning

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