@Article{cmc.2020.09722,
AUTHOR = {Zhichun Jia, Qiuyang Han, Yanyan Li, Yuqiang Yang, Xing Xing},
TITLE = {Prediction of Web Services Reliability Based on Decision Tree  Classification Method},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {63},
YEAR = {2020},
NUMBER = {3},
PAGES = {1221--1235},
URL = {http://www.techscience.com/cmc/v63n3/38871},
ISSN = {1546-2226},
ABSTRACT = {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 
enterprises. Nowadays, 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 function. So it is necessary to design feasible selection 
strategies to provide users with the reliable services. Most existing methods attempt to 
select services according to accurate predictions for the quality of service (QoS) values. 
However, because the network and user needs are dynamic, it is almost impossible to 
accurately predict the QoS values. Furthermore, accurate prediction is generally timeconsuming. This paper proposes a service decision tree based post-pruning prediction 
approach. This paper first defines the five reliability levels for measuring the reliability of 
services. By 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 model. Using 
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 
service. Moreover, this paper applies the post-pruning strategy to optimize the generated 
model for avoiding the over-fitting. Experimental results show that the proposed method 
is effective in predicting the service reliability.},
DOI = {10.32604/cmc.2020.09722}
}