Trustworthiness prediction of cloud services based on selective neural network ensemble learning

doi:10.1016/j.eswa.2020.114390

CORC > 深海科学与工程研究所 > 中国科学院深海科学与工程研究所 > 管理支撑 > 科学技术处

	Trustworthiness prediction of cloud services based on selective neural network ensemble learning
	Mao, Chengying 3; Lin, Rongru 2; Towey, Dave 1; Wang, Wenle 5; Chen, Jifu 3; He, Qiang 4
刊名	EXPERT SYSTEMS WITH APPLICATIONS
	2021-04-15
卷号	168 页码:17
关键词	Cloud services Trustworthiness prediction Selective ensemble learning Neural networks Particle swarm optimization (PSO)
ISSN号	0957-4174
DOI	10.1016/j.eswa.2020.114390
英文摘要	Cloud services have become a popular and flexible solution for providing components to build service-based systems. A component's trustworthiness is a key measure that can guide service requesters when making a service selection decision. Prediction of this trustworthiness, based on the component's multi-faceted quality of service (QoS) attributes, is therefore an important problem to address. In this paper, selective ensemble learning is introduced to address the trust problem for cloud services: We use back-propagation neural networks (BPNNs) as the basic classifiers, with two swarm intelligence algorithms adapted to search for the optimal aggregation weights to create the ensemble: Basic particle swarm optimization (PSO) is used for decimal weights; and quantum discrete PSO (QPSO) is used for binary (0-1) weights. The optimized ensemble learning model, based on BPNNs, is then used to predict the trustworthiness of a given cloud service. Extensive experiments are performed on a well-known, public dataset to verify the effectiveness of the proposed trust prediction algorithms. The experimental results show that our algorithms are not only better than the basic BPNN method in prediction precision, but also outperform current state-of-the-art trust prediction algorithms. The proposed algorithms also exhibit a strong robustness.
资助项目	National Natural Science Foundation of China[61762040] ; National Natural Science Foundation of China[61872167] ; Natural Science Foundation of Jiangxi Province, China[20162BCB23036] ; Natural Science Foundation of Jiangxi Province, China[20171ACB21031] ; Science Foundation of the Jiangxi Educational Committee, China[GJJ180276] ; Jiangxi Social Science Research Project, China[TQ-2015-202]
WOS研究方向	Computer Science ; Engineering ; Operations Research & Management Science
语种	英语
出版者	PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号	WOS:000614253700010
资助机构	National Natural Science Foundation of China ; Natural Science Foundation of Jiangxi Province, China ; Science Foundation of the Jiangxi Educational Committee, China ; Jiangxi Social Science Research Project, China
内容类型	期刊论文
源URL	[http://ir.idsse.ac.cn/handle/183446/8353]
专题	科学技术处
通讯作者	Mao, Chengying
作者单位	1.Univ Nottingham Ningbo China, Sch Comp Sci, Ningbo 315100, Peoples R China 2.Chinese Acad Sci, Inst Deep Sea Sci & Engn, Sanya 572000, Peoples R China 3.Jiangxi Univ Finance & Econ, Sch Software & IoT Engn, Nanchang 330013, Jiangxi, Peoples R China 4.Swinburne Univ Technol, Dept Comp Sci & Software Engn, Melbourne, Vic 3122, Australia 5.Jiangxi Normal Univ, Sch Software, Nanchang 330022, Jiangxi, Peoples R China
推荐引用方式 GB/T 7714	Mao, Chengying,Lin, Rongru,Towey, Dave,et al. Trustworthiness prediction of cloud services based on selective neural network ensemble learning[J]. EXPERT SYSTEMS WITH APPLICATIONS,2021,168:17.
APA	Mao, Chengying,Lin, Rongru,Towey, Dave,Wang, Wenle,Chen, Jifu,&He, Qiang.(2021).Trustworthiness prediction of cloud services based on selective neural network ensemble learning.EXPERT SYSTEMS WITH APPLICATIONS,168,17.
MLA	Mao, Chengying,et al."Trustworthiness prediction of cloud services based on selective neural network ensemble learning".EXPERT SYSTEMS WITH APPLICATIONS 168(2021):17.