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JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE)
Service Computing     
Web services QoS prediction method based on Bayes classification
REN Di, WAN Jian, YIN Yu-yu, ZHOU Li, GAO Min
1. School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China; 2. Key Laboratory of Complex Systems Modeling and Simulation of Ministry of Education, Hangzhou 310018, China
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Abstract  A novel hybrid collaborative filtering quality of service (QoS) prediction method based on Bayes classification was proposed in order to address the problem that network instability may lead to some noisy QoS data in real environment, and the utilization of noisy data would decrease the prediction accuracy greatly. This method first employed Bayes algorithm to classify Web service QoS data and compute the probability of every classification. Then the possible range of the missing QoS value could be identified. The similar neighbors were filtered according to the range. At last, the traditional collaborative filtering algorithm was improved to compute the final prediction results by using the probability of the classifications. To some extent, the proposed method can reduce the impact of the noisy data. Compared with the existing methods, the experimental results demonstrate that our method can achieve higher prediction accuracy.

Published: 11 June 2017
CLC:  TP 312  
Cite this article:

REN Di, WAN Jian, YIN Yu-yu, ZHOU Li, GAO Min. Web services QoS prediction method based on Bayes classification. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(6): 1242-1251.


基于贝叶斯分类的Web服务质量预测方法研究

针对网络环境不稳定导致Web服务质量(QoS)数据中存在噪声数据,进而降低Web服务质量预测精度的问题,提出一种基于贝叶斯分类的混合协同过滤Web服务质量值预测方法.该方法使用贝叶斯算法对Web服务质量数据进行分类并得到每个分类的概率,利用分类结果确定缺失值可能的取值范围,并对用户和服务的相似邻居进行过滤.通过引入分类概率,改进传统的协同过滤方法得到最终的缺失值预测结果,在一定程度上消除了噪声数据对Web服务质量预测的影响.实验结果表明:较之现有方法,该方法具有更好的预测精度.

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