LP-MLTSVM: Laplacian Multi-Label Twin Support Vector Machine for Semi-Supervised Classification
In the machine learning jargon, multi-label classification refers to a task where multiple mutually non-exclusive class labels are assigned to a single instance. Generally, the lack of sufficient labeled training data demanded by a classification task is met by an approach known as semi-supervised l...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9667364/ |
| _version_ | 1818894065893113856 |
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| author | Farhad Gharebaghi Ali Amiri |
| author_facet | Farhad Gharebaghi Ali Amiri |
| author_sort | Farhad Gharebaghi |
| collection | DOAJ |
| description | In the machine learning jargon, multi-label classification refers to a task where multiple mutually non-exclusive class labels are assigned to a single instance. Generally, the lack of sufficient labeled training data demanded by a classification task is met by an approach known as semi-supervised learning. This type of learning extracts the decision rules of classification by utilizing both labeled and unlabeled data. Regarding multi-label data, however, current semi-supervised learning methods are unable to classify them accurately. Therefore, with the goal of generalizing the state-of-the-art semi-supervised approaches to multi-label data, this paper proposes a novel two-stage method for multi-label semi-supervised classification. The first stage determines the label(s) of the unlabeled training data by means of a smooth graph constructed using the manifold regularization. In the second stage, thanks to the capability of the twin support vector machine to relax the requirement that hyperplanes should be parallel in classical SVM, we employ it to establish a multi-label classifier called LP-MLTSVM. In the experiments, this classifier is applied on benchmark datasets. The simulation results substantiate that compared to the existing multi-label classification algorithms, LP-MLTSVM shows superior performance in terms of the Hamming loss, average precision, coverage, ranking loss, and one-error metrics. |
| first_indexed | 2024-12-19T18:22:34Z |
| format | Article |
| id | doaj.art-f57bdb6060794bf3b1aa32b05f388841 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T18:22:34Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f57bdb6060794bf3b1aa32b05f3888412022-12-21T20:10:57ZengIEEEIEEE Access2169-35362022-01-0110137381375210.1109/ACCESS.2021.31399299667364LP-MLTSVM: Laplacian Multi-Label Twin Support Vector Machine for Semi-Supervised ClassificationFarhad Gharebaghi0https://orcid.org/0000-0002-0919-5831Ali Amiri1https://orcid.org/0000-0003-2297-8707Department of Computer Engineering, Faculty of Computer and Information Technology Engineering, Islamic Azad University, Qazvin Branch, Qazvin, IranDepartment of Computer Engineering, University of Zanjan, Zanjan, IranIn the machine learning jargon, multi-label classification refers to a task where multiple mutually non-exclusive class labels are assigned to a single instance. Generally, the lack of sufficient labeled training data demanded by a classification task is met by an approach known as semi-supervised learning. This type of learning extracts the decision rules of classification by utilizing both labeled and unlabeled data. Regarding multi-label data, however, current semi-supervised learning methods are unable to classify them accurately. Therefore, with the goal of generalizing the state-of-the-art semi-supervised approaches to multi-label data, this paper proposes a novel two-stage method for multi-label semi-supervised classification. The first stage determines the label(s) of the unlabeled training data by means of a smooth graph constructed using the manifold regularization. In the second stage, thanks to the capability of the twin support vector machine to relax the requirement that hyperplanes should be parallel in classical SVM, we employ it to establish a multi-label classifier called LP-MLTSVM. In the experiments, this classifier is applied on benchmark datasets. The simulation results substantiate that compared to the existing multi-label classification algorithms, LP-MLTSVM shows superior performance in terms of the Hamming loss, average precision, coverage, ranking loss, and one-error metrics.https://ieeexplore.ieee.org/document/9667364/Multi-label classificationsemi-supervised learningsmooth graphGraph Laplacianmanifold regularizationtwin support vector machine |
| spellingShingle | Farhad Gharebaghi Ali Amiri LP-MLTSVM: Laplacian Multi-Label Twin Support Vector Machine for Semi-Supervised Classification IEEE Access Multi-label classification semi-supervised learning smooth graph Graph Laplacian manifold regularization twin support vector machine |
| title | LP-MLTSVM: Laplacian Multi-Label Twin Support Vector Machine for Semi-Supervised Classification |
| title_full | LP-MLTSVM: Laplacian Multi-Label Twin Support Vector Machine for Semi-Supervised Classification |
| title_fullStr | LP-MLTSVM: Laplacian Multi-Label Twin Support Vector Machine for Semi-Supervised Classification |
| title_full_unstemmed | LP-MLTSVM: Laplacian Multi-Label Twin Support Vector Machine for Semi-Supervised Classification |
| title_short | LP-MLTSVM: Laplacian Multi-Label Twin Support Vector Machine for Semi-Supervised Classification |
| title_sort | lp mltsvm laplacian multi label twin support vector machine for semi supervised classification |
| topic | Multi-label classification semi-supervised learning smooth graph Graph Laplacian manifold regularization twin support vector machine |
| url | https://ieeexplore.ieee.org/document/9667364/ |
| work_keys_str_mv | AT farhadgharebaghi lpmltsvmlaplacianmultilabeltwinsupportvectormachineforsemisupervisedclassification AT aliamiri lpmltsvmlaplacianmultilabeltwinsupportvectormachineforsemisupervisedclassification |