Open Access

REVIEW

Ensemble Learning Models for Classification and Selection of Web Services: A Review

Muhammad Hasnain¹, Imran Ghani², Seung Ryul Jeong^3,*, Aitizaz Ali¹

1 Monash University, Petaling Jaya, 46150, Malaysia
2 Indiana University of Pennsylvania, Indiana, PA 15705, USA
3 Kookmin University, Seoul, 02707, Korea

* Corresponding Author: Seung Ryul Jeong. Email:

Computer Systems Science and Engineering 2022, 40(1), 327-339. https://doi.org/10.32604/csse.2022.018300

Received 04 March 2021; Accepted 30 April 2021; Issue published 26 August 2021

Abstract

This paper presents a review of the ensemble learning models proposed for web services classification, selection, and composition. Web service is an evolutionary research area, and ensemble learning has become a hot spot to assess web services’ earlier mentioned aspects. The proposed research aims to review the state of art approaches performed on the interesting web services area. The literature on the research topic is examined using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) as a research method. The study reveals an increasing trend of using ensemble learning in the chosen papers within the last ten years. Naïve Bayes (NB), Support Vector Machine’ (SVM), and other classifiers were identified as widely explored in selected studies. Core analysis of web services classification suggests that web services’ performance aspects can be investigated in future works. This paper also identified performance measuring metrics, including accuracy, precision, recall, and f-measure, widely used in the literature.

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Keywords

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