Laplacian Twin Support Vector Machine With Pinball Loss for Semi-Supervised Classification
Semi-supervised learning utilizes labeled data and the geometric information in the unlabeled data to construct a model whereas supervised learning makes use of the only label data. So, semi-supervised learning establishes a more reasonable classifier. In recent years, the Laplacian support vector m...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10082928/ |
| _version_ | 1797852992633831424 |
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| author | Vipavee Damminsed Wanida Panup Rabian Wangkeeree |
| author_facet | Vipavee Damminsed Wanida Panup Rabian Wangkeeree |
| author_sort | Vipavee Damminsed |
| collection | DOAJ |
| description | Semi-supervised learning utilizes labeled data and the geometric information in the unlabeled data to construct a model whereas supervised learning makes use of the only label data. So, semi-supervised learning establishes a more reasonable classifier. In recent years, the Laplacian support vector machine (Lap-SVM) has received a lot of interest in the framework of semi-supervised classification. To develop the performance of Lap-SVM, Laplacian twin support vector machine (Lap-TSVM) has shown exceptional performance as an addition to improve the computational complexity. However, dealing with noise sensitivity and instability for resampling due to its hinge loss function is still a challenge. In this paper, we provide a novel Laplacian twin support vector machine by combining pinball loss function, termed as Lap-PTSVM, to effectively handle the aforementioned problems. As a result, it improves a better generalization ability of the classifier. Several experiments have been performed on artificial and UCI datasets. The results show that the proposed model has noise insensitivity comparable to the Lap-TSVM and has a great generalization performance. Furthermore, non-parametric statistical test are also conducted to justify the competitive performance of Lap-PTSVM. |
| first_indexed | 2024-04-09T19:43:22Z |
| format | Article |
| id | doaj.art-897bf8fc8aa344f8ab44722f106414d4 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-09T19:43:22Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-897bf8fc8aa344f8ab44722f106414d42023-04-03T23:00:37ZengIEEEIEEE Access2169-35362023-01-0111313993141610.1109/ACCESS.2023.326227010082928Laplacian Twin Support Vector Machine With Pinball Loss for Semi-Supervised ClassificationVipavee Damminsed0Wanida Panup1Rabian Wangkeeree2https://orcid.org/0000-0002-5715-3804Department of Mathematics, Naresuan University, Phitsanulok, ThailandGeo-Informatics and Space Technology Development Agency (GISTDA), Chonburi, ThailandDepartment of Mathematics, Naresuan University, Phitsanulok, ThailandSemi-supervised learning utilizes labeled data and the geometric information in the unlabeled data to construct a model whereas supervised learning makes use of the only label data. So, semi-supervised learning establishes a more reasonable classifier. In recent years, the Laplacian support vector machine (Lap-SVM) has received a lot of interest in the framework of semi-supervised classification. To develop the performance of Lap-SVM, Laplacian twin support vector machine (Lap-TSVM) has shown exceptional performance as an addition to improve the computational complexity. However, dealing with noise sensitivity and instability for resampling due to its hinge loss function is still a challenge. In this paper, we provide a novel Laplacian twin support vector machine by combining pinball loss function, termed as Lap-PTSVM, to effectively handle the aforementioned problems. As a result, it improves a better generalization ability of the classifier. Several experiments have been performed on artificial and UCI datasets. The results show that the proposed model has noise insensitivity comparable to the Lap-TSVM and has a great generalization performance. Furthermore, non-parametric statistical test are also conducted to justify the competitive performance of Lap-PTSVM.https://ieeexplore.ieee.org/document/10082928/Laplacian twin support vector machinepinball loss functionsemi-supervised classification |
| spellingShingle | Vipavee Damminsed Wanida Panup Rabian Wangkeeree Laplacian Twin Support Vector Machine With Pinball Loss for Semi-Supervised Classification IEEE Access Laplacian twin support vector machine pinball loss function semi-supervised classification |
| title | Laplacian Twin Support Vector Machine With Pinball Loss for Semi-Supervised Classification |
| title_full | Laplacian Twin Support Vector Machine With Pinball Loss for Semi-Supervised Classification |
| title_fullStr | Laplacian Twin Support Vector Machine With Pinball Loss for Semi-Supervised Classification |
| title_full_unstemmed | Laplacian Twin Support Vector Machine With Pinball Loss for Semi-Supervised Classification |
| title_short | Laplacian Twin Support Vector Machine With Pinball Loss for Semi-Supervised Classification |
| title_sort | laplacian twin support vector machine with pinball loss for semi supervised classification |
| topic | Laplacian twin support vector machine pinball loss function semi-supervised classification |
| url | https://ieeexplore.ieee.org/document/10082928/ |
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