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International Journal of Nonlinear Analysis and Applications، جلد ۱۳، شماره ۱، صفحات ۲۸۷۱-۲۸۸۳
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عنوان انگلیسی Integration of deep learning model and feature selection for multi-label classification
چکیده انگلیسی مقاله 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.
کلیدواژه‌های انگلیسی مقاله machine learning, Classification, Multi-Label, Meta-Label-Specific Features, Deep learning
نویسندگان مقاله Hossein Ebrahimi |
Department of IT and Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran

Kambiz Majidzadeh |
Assistant Professor, Department of IT and Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran

Farhad Soleimanian Gharehchopogh |
Assistant Professor, Department of IT and Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran

نشانی اینترنتی https://ijnaa.semnan.ac.ir/article_6014_ff8c4332ed9792952f6fc20404b8b8d9.pdf
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