Enhancing the accuracy of quality-based Web Services classification through dataset feature enhancement

Subject Areas : Journal of Computer & Robotics

mehdi nozad bonab ¹ , jafar tanha ² , mohammad masdari ³

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Received: 2024-08-22 Accepted : 2024-10-06

Keywords: Web services, classification, quality, feature engineering, machine learning,

Abstract :

Web services, which facilitate machine-to-machine communication over the Internet, require accurate classification for reliable and efficient service delivery. The classification significantly affects service discovery, recommendation systems, and service composition. Web service brokers assist users in selecting the most suitable service based on quality parameters. Currently, only a limited number of datasets focusing on web service quality are available. The QWS dataset, with nine quality features for services, is one of the most prominent datasets in this field. However, this dataset overlooks non-functional attributes such as Security, Interoperability, Scalability, and Robustness, which are essential for discovering web services. This paper proposes enhancing the QWS dataset by using feature engineering to create new features from existing ones. The experimental results on the SSL-WSC algorithm demonstrate that the proposed approach significantly improves the classification of web services. This is evidenced by the 5.05% increase in F1-Score, 5.69% increase in accuracy, and 6.92% increase in precision evaluation criteria.

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