Multi-Label Classification with Meta-Label-Specific Features and Q-Learning

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Seyed Ebrahimi Seyed Hossein; Majidzadeh Kambiz; SOLEIMANIAN GHAREHCHOPOGH FARHAD

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Journal Paper

Paper Information

Journal: CONTROL AND OPTIMIZATION IN APPLIED MATHEMATICS Year:2021 | Volume:6 | Issue:2 Page(s): 37-52

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Information Journal Paper

Title

Multi-Label Classification with Meta-Label-Specific Features and Q-Learning

Author(s)

Seyed Ebrahimi Seyed Hossein | Majidzadeh Kambiz | SOLEIMANIAN GHAREHCHOPOGH FARHAD | Issue Writer Certificate

Keywords

Machine learning

Classification

Multi-label

Meta-label specific features

Abstract

Classification is a crucial process in data mining, data science, Machine learning, and the applications of natural language processing. Classification methods distinguish the correlation between the data and the output classes. In single-label Classification (SLC), each input sample is associated with only one class label. In certain real-world applications, data instances may be assigned to more than one class. The type of Classification which is required in such applications is known as Multi-label Classification (MLC). In MLC, each sample of data is associated with a set of labels. Due to the presence of multiple class labels, the SLC learning process is not applicable to MLC tasks. Many solutions to the Multi-label Classification problem have been proposed, including BR, FS-DR, and LLSF. But, these methods are not as accurate as they could be. In this paper, a new Multi-label Classification method is proposed based on graph representation. A feature selection technique and the Q-learning method are employed to increase the accuracy of the proposed algorithm. The proposed Multi-label Classification algorithm is applied to various standard Multi-label datasets. The results are compared with state-of-the-art algorithms based on the well-known performance evaluation metrics. Experimental results demonstrated the effectiveness of the proposed model and its superiority over the other methods.

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APA: Copy

Seyed Ebrahimi, Seyed Hossein, Majidzadeh, Kambiz, & SOLEIMANIAN GHAREHCHOPOGH, FARHAD. (2021). Multi-Label Classification with Meta-Label-Specific Features and Q-Learning. CONTROL AND OPTIMIZATION IN APPLIED MATHEMATICS, 6(2), 37-52. SID. https://sid.ir/paper/1058335/en

Vancouver: Copy

Seyed Ebrahimi Seyed Hossein, Majidzadeh Kambiz, SOLEIMANIAN GHAREHCHOPOGH FARHAD. Multi-Label Classification with Meta-Label-Specific Features and Q-Learning. CONTROL AND OPTIMIZATION IN APPLIED MATHEMATICS[Internet]. 2021;6(2):37-52. Available from: https://sid.ir/paper/1058335/en

IEEE: Copy

Seyed Hossein Seyed Ebrahimi, Kambiz Majidzadeh, and FARHAD SOLEIMANIAN GHAREHCHOPOGH, “Multi-Label Classification with Meta-Label-Specific Features and Q-Learning,” CONTROL AND OPTIMIZATION IN APPLIED MATHEMATICS, vol. 6, no. 2, pp. 37–52, 2021, [Online]. Available: https://sid.ir/paper/1058335/en

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