Prediction of Web Services Reliability Based on Decision Tree Classification Method

作     者：Zhichun Jia Qiuyang Han Yanyan Li Yuqiang Yang Xing Xing 

作者机构：College of Information Science and TechnologyBohai UniversityJinzhou121013China School of EngineeringThe University of New MexicoAlbuquerqueNM 87131USA 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2020年第63卷第6期

页      面：1221-1235页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This paper is partially supported by the National Natural Science Foundation of China under Grant No.61972053 and No.61603054 by the Scientific Research Foundation of Liaoning Education Department under Grant No.LQ2019016,No.LJ2019015 by the Natural Science Foundation of Liaoning Province,China under Grant No.2019-ZD-0505 

主　　题：Decision tree reliability level quality of service continuous attribute 

摘      要：With the development of the service-oriented computing(SOC),web service has an important and popular solution for the design of the application system to various ***,the numerous web services are provided by the service providers on the network,it becomes difficult for users to select the best reliable one from a large number of services with the same *** it is necessary to design feasible selection strategies to provide users with the reliable *** existing methods attempt to select services according to accurate predictions for the quality of service(QoS)***,because the network and user needs are dynamic,it is almost impossible to accurately predict the QoS ***,accurate prediction is generally *** paper proposes a service decision tree based post-pruning prediction *** paper first defines the five reliability levels for measuring the reliability of *** analyzing the quality data of service from the network,the proposed method can generate the training set and convert them into the service decision tree *** the generated model and the given predicted services,the proposed method classifies the service to the corresponding reliability level after discretizing the continuous attribute of ***,this paper applies the post-pruning strategy to optimize the generated model for avoiding the *** results show that the proposed method is effective in predicting the service reliability.