TY  - EJOU
AU  - Hasnain, Muhammad 
AU  - Ghani, Imran 
AU  - Jeong, Seung Ryul 
AU  - Ali, Aitizaz 

TI  - Ensemble Learning Models for Classification and Selection of Web Services: A Review
T2  - Computer Systems Science and Engineering

PY  - 2022
VL  - 40
IS  - 1
SN  - 

AB  - 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.
KW  - Web services composition; quality improvement; class imbalance; machine learning

DO  - 10.32604/csse.2022.018300