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

作　　者：Muhammad Hasnain Imran Ghani Seung Ryul Jeong Aitizaz Ali 

机构地区：[1]Monash University,Petaling Jaya,46150,Malaysia [2]Indiana University of Pennsylvania,Indiana,PA 15705,USA [3]Kookmin University,Seoul,02707,Korea

出　　处：《Computer Systems Science & Engineering》2022年第1期327-339,共13页计算机系统科学与工程（英文）

基　　金：This research was supported by the BK21 FOUR(Fostering Outstanding Universities for Research);the Ministry of Education(MOE,Korea)and National Research Foundation of Korea(NRF).

摘　　要：This paper presents a review of the ensemble learning models proposed for web services classification,selection,and composition.Web service is an evo-lutionary 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.

关 键 词：Web services composition quality improvement class imbalance machine learning 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]