A Semi-Supervised Learning Approach to Quality-Based Web Service Classification
The Internet provides a platform for sharing services, and web service brokers help users to choose the suitable service among similar services based on ranking. The quality of service is important in evaluating the services the user needs. However, finding a quality-based data label in many fields...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10491237/ |
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| author | Mehdi Nozad Bonab Jafar Tanha Mohammad Masdari |
| author_facet | Mehdi Nozad Bonab Jafar Tanha Mohammad Masdari |
| author_sort | Mehdi Nozad Bonab |
| collection | DOAJ |
| description | The Internet provides a platform for sharing services, and web service brokers help users to choose the suitable service among similar services based on ranking. The quality of service is important in evaluating the services the user needs. However, finding a quality-based data label in many fields can be time-consuming and difficult. Thus, machine learning is required to classify and choose the best service in this field. The selection process is done through analysis and recommendations by the system. This article introduces the SSL-WSC algorithm, which classifies unlabeled data through semi-supervised self-training learning using a small amount of labeled data. This algorithm labels the data using a two-step method of calculating a score for each service and dynamic thresholding. The quality features of web services obtained from the QWS dataset were used to evaluate the performance of the proposed algorithm. The experimental results in different scenarios showed that using proposed semi-supervised learning algorithms to create classification models led to better results, so it improved the F1-score, accuracy, and precision, on average, by 11.26%, 9.43% and 9.53%, respectively, as compared to the supervised method. |
| format | Article |
| id | doaj-art-8d9ab7cce3d14034ac09cc9461102dbc |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-8d9ab7cce3d14034ac09cc9461102dbc2025-01-31T23:04:24ZengIEEEIEEE Access2169-35362024-01-0112504895050310.1109/ACCESS.2024.338534110491237A Semi-Supervised Learning Approach to Quality-Based Web Service ClassificationMehdi Nozad Bonab0https://orcid.org/0000-0003-2398-2185Jafar Tanha1https://orcid.org/0000-0002-0779-6027Mohammad Masdari2https://orcid.org/0000-0002-7093-2204Department of Computer Engineering, Islamic Azad University, Urmia Branch, Urmia, IranElectrical and Computer Engineering Department, University of Tabriz, Tabriz, IranDepartment of Computer Engineering, Islamic Azad University, Urmia Branch, Urmia, IranThe Internet provides a platform for sharing services, and web service brokers help users to choose the suitable service among similar services based on ranking. The quality of service is important in evaluating the services the user needs. However, finding a quality-based data label in many fields can be time-consuming and difficult. Thus, machine learning is required to classify and choose the best service in this field. The selection process is done through analysis and recommendations by the system. This article introduces the SSL-WSC algorithm, which classifies unlabeled data through semi-supervised self-training learning using a small amount of labeled data. This algorithm labels the data using a two-step method of calculating a score for each service and dynamic thresholding. The quality features of web services obtained from the QWS dataset were used to evaluate the performance of the proposed algorithm. The experimental results in different scenarios showed that using proposed semi-supervised learning algorithms to create classification models led to better results, so it improved the F1-score, accuracy, and precision, on average, by 11.26%, 9.43% and 9.53%, respectively, as compared to the supervised method.https://ieeexplore.ieee.org/document/10491237/Classificationmachine learningqualitysemi-supervised learningweb services |
| spellingShingle | Mehdi Nozad Bonab Jafar Tanha Mohammad Masdari A Semi-Supervised Learning Approach to Quality-Based Web Service Classification IEEE Access Classification machine learning quality semi-supervised learning web services |
| title | A Semi-Supervised Learning Approach to Quality-Based Web Service Classification |
| title_full | A Semi-Supervised Learning Approach to Quality-Based Web Service Classification |
| title_fullStr | A Semi-Supervised Learning Approach to Quality-Based Web Service Classification |
| title_full_unstemmed | A Semi-Supervised Learning Approach to Quality-Based Web Service Classification |
| title_short | A Semi-Supervised Learning Approach to Quality-Based Web Service Classification |
| title_sort | semi supervised learning approach to quality based web service classification |
| topic | Classification machine learning quality semi-supervised learning web services |
| url | https://ieeexplore.ieee.org/document/10491237/ |
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