A HYBRID MODEL FOR WEB SERVICE CLASSIFICATION USING SSL AND CATBOOST

Boggavarapu Naga Lakshmi Prasanna, M V Ramana

Abstract


The proliferation of web services has made it increasingly difficult for users to identify high-quality services at competitive prices. To address this challenge, the base study proposes a Semi-Supervised Learning algorithm for Web Service Classification (SSL-WSC), which classifies services into four quality tiers Platinum, Gold, Silver, and Bronze using limited labeled data and a Mahalanobis-based confidence scoring mechanism. While effective, the original model does not leverage advanced machine learning techniques. As an extension, this work incorporates the CatBoost algorithm, known for its ability to handle categorical data and prevent overfitting through ordered boosting. Experimental results on the QWS dataset demonstrate that CatBoost achieves over 95% classification accuracy, significantly outperforming both traditional classifiers and the SSL-WSC baseline. This integration highlights the potential of combining semi-supervised frameworks with modern ensemble methods to enhance prediction reliability in service quality classification. The model is deployed via a Flask-based interface for interactive real-time classification.

References


S. E. El-Sayyad, A. I. Saleh, and H. A. Ali, ‘‘A new semantic web service classification (SWSC) strategy,’’ Cluster Comput., vol. 21, no. 3, pp. 1639–1665, Sep. 2018.

K. C. Li, Y. Xia, F. Xie, W. Liang, and M. Tang, ‘‘Predicting new composition relations between web services via link analysis,’’ Int. J. Comput. Sci. Eng., vol. 20, no. 1, p. 88, 2019.

B. Al-Shargabi, S. Al-Jawarneh, and S. Hayajneh, ‘‘A cloudlet based security and trust model for e-government web services,’’ J. Theor. Appl. Inf. Technol., vol. 98, no. 1, pp. 27–37, 2020.

H. Ye, ‘‘Web services classification based on wide & Bi-LSTM model,’’ IEEE Access, vol. 7, pp. 43697–43706, 2019.

K. Zhao, J. Liu, Z. Xu, X. Liu, L. Xue, Z. Xie, Y. Zhou, and X. Wang, ‘‘Graph4Web: A relation-aware graph attention network for web service classification,’’ J. Syst. Softw., vol. 190, Aug. 2022, Art. no. 111324.

M. Masdari, M. N. Bonab, and S. Ozdemir, ‘‘Correction to: QoS-driven metaheuristic service composition schemes: A comprehensive overview,’’ Artif. Intell. Rev., vol. 55, no. 2, p. 1605, Feb. 2022.

Q. She, X. Wei, G. Nie, and D. Chen, ‘‘QoS-aware cloud service composition: A systematic mapping study from the perspective of computational intelligence,’’ Expert Syst. Appl., vol. 138, Dec. 2019, Art. no. 112804.

P. B. Pandharbale, S. N. Mohanty, and A. K. Jagadev, ‘‘QoS-aware web services recommendations using dynamic clustering algorithms,’’ Int. J. Inf. Syst. Model. Design, vol. 13, no. 6, pp. 1–16, Sep. 2022.

P. Bagga, A. Joshi, and R. Hans, ‘‘QoS based web service selection and multi-criteria decision making methods,’’ Int. J. Interact. Multimedia Artif.Intell., vol. 5, no. 4, p. 113, 2019.

S. Rangarajan and R. K. Chandar, ‘‘QoS-based architecture for discovery and selection of suitable web services using non-functional properties,’’ ICST Trans. Scalable Inf. Syst., vol. 4, no. 12, Jan. 2017, Art. no. 152102.

M. Ghobaei-Arani, A. A. Rahmanian, M. S. Aslanpour, and S. E. Dashti, ‘‘CSA-WSC: Cuckoo search algorithm for web service composition in cloud environments,’’ Soft Comput., vol. 22, no. 24, pp. 8353–8378, Dec. 2018.

Z. Ivezić, Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data. Princeton, NJ, USA: Princeton Univ. Press, 2019.

S. L. Brunton, B. R. Noack, and P. Koumoutsakos, ‘‘Machine learning for fluid mechanics,’’ Annu. Rev. Fluid Mech., vol. 52, no. 1, pp. 477–508, Jan. 2020.

M. J. Kaur, V. P. Mishra, and P. Maheshwari, ‘‘The convergence of digital twin, IoT, and machine learning: Transforming data into action,’’ in Digital Twin Technologies and Smart Cities. Cham, Switzerland: Springer, 2020, pp. 3–17.

M. Hasnain, ‘‘Machine learning methods for trust-based selection of web services,’’ KSII Trans. Internet Inf. Syst., vol. 16, no. 1, pp. 38–59, 2022.

Y. Qin, S. Ding, L. Wang, and Y. Wang, ‘‘Research progress on semisupervised clustering,’’ Cognit.Comput., vol. 11, no. 5, pp. 599–612, Oct. 2019.

S. Pattanayak and S. John, Pro Deep Learning With Tensorflow. Cham, Switzerland: Springer, 2017.

S. Shalev-Shwartz and S. Ben-David, Understanding Machine Learning: From Theory to Algorithms. Cambridge, U.K.: Cambridge Univ. Press, 2014.

J. M. Duarte and L. Berton, ‘‘A review of semi-supervised learning for text classification,’’ Artif.Intell. Rev., vol. 56, no. 9, pp. 9401–9469, Sep. 2023.

S. Khezri, J. Tanha, A. Ahmadi, and A. Sharifi, ‘‘STDS: Self-training data streams for mining limited labeled data in non-stationary environment,’’ Appl. Intell., vol. 50, no. 5, pp. 1448–1467, May 2020.


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