一种领域自适应的Web服务分类方法
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摘要
如何提高Web服务分类的准确性是当前服务分类方法研究的热点之一。基于机器学习的Web服务分类方法存在由于源域与目标域的分布不同而导致分类准确性下降问题。论文基于KNN算法,设计了一种领域自适应的Web服务分类方法,通过动态调整源域数据,达到领域自适应Web服务分类且提高分类准确性的目的。
How to improve the accuracy of Web service classification is one of the hot topics in current service classification methods. The Web service classification method based on machine learning can lead to the decline of classification accuracy due to the different distribution of source domain and target domain. A kind of domain adaptive Web service classification method is designed based on KNN(K-NearestNeighbor)algorithm,by dynamically adjusting the source domain data,so as to achieve domain adaptive Web service classification and enhance the classification accuracy.
How to improve the accuracy of Web service classification is one of the hot topics in current service classification methods. The Web service classification method based on machine learning can lead to the decline of classification accuracy due to the different distribution of source domain and target domain. A kind of domain adaptive Web service classification method is designed based on KNN(K-NearestNeighbor)algorithm,by dynamically adjusting the source domain data,so as to achieve domain adaptive Web service classification and enhance the classification accuracy.
引文
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[3]胡杨,冯旭鹏,戴丹,等.最小费用最大流跨领域情感分类框架[J].小型微型计算机系统,2017,38(1):49-55.HU Yang,FENG Xupeng,DAI Dan,et al.Minimum-cost and Maximum-flow Framework for Cross-domain Sentiment Classification[J].Journal of Chinese Computer System,2017,38(1):49-55.
[4]Z Deng,XZhu,D Cheng,MZong,S Zhang.Efficient kNNclassification algorithm for big data[J].Neurocomputing,2016,195(C):143-148.
[5]Mahdi Bazarganigilani,Belinda Fridey,Ali Syed.Web Service Intrusion Detection Using XML Similarity Classification and WSDL Description[J].International Journal of u-and e-Service,Science and Technology,2011,4(3):61-72.
[6]C Sepulveda,R Alarcon,J Bellido.QoS aware descriptions for RESTful service composition:security domain[J].World Wide Web-internet&Web Information Systems,2015,18(4):767-794
[7]ZHENG Li,Liam O'Brien.Towards Effort Estimation for Web Service Compositions using Classification Matrix[J].International Journal on Advances in Internet Technology,2011,3(3&4):245-260-245-260.
[8]苏金树,张博锋,徐昕.基于机器学习的文本分类技术研究进展[J].软件学报,2006,17(9):1848-1859.SU Jinshu,ZHANG Bofeng,XU Xin.Advances in Machine Learning Based Text Categorization[J].Journal of Software,2006,17(9):1848-1859.
[9]HE Qing,LI Ning,LUO Wenjuan,SHI Zhongzhi.A survey of machine learning algorithms for big data[J].Pattern Recognition and Artificial Intelligence,2014,27(4):327-336.
[10]王实,高文.增强型朴素贝叶斯学习[J].计算机科学,2000,27(4):46-49.WANG Shi,GAO Wen.Boosting Naive Bayesian Learning[J].Computer Science,2000,27(4):46-49.
[11]孙丽华,张积东,李静梅.一种改进的KNN方法及其在文本分类中的应用[J].应用科技,2002,29(2):25-27.SUN Lihua,ZHANG Jidong,LI jingmei.An Improved k-Nearest Neighbor System and Its Application to Text Classification[J].Applied Science And Technology,2002,29(2):25-27.
[12]张宁,贾自艳,史植忠.使用KNN算法的文本分类[J].计算机工程,2005,31(8):171-172.ZHANG Ning,JIA Ziyan,SHI Zhongzhi.Text Categorization with KNN Algorithm[J].Computer Engineering,2005,31(8):171-172.
[13]李洪奇,杨中国,朱丽萍,等.基于数据集特征的KNN最优K值预测方法[J].计算机应用与软件,2016,33(6):54-58.LI Hongqi,YANG Zhongguo,ZHU Liping,et al.Prediction method of Optimal K Value in KNN Based on Dataset Features[J].Computer Application and Software,2016,33(6):54-58.
[14]T Abeywickrama,MA Cheema,D Taniar.k-Nearest Neighbors on Road Networks:A Journey inExperimentation and In-Memory Implementation[J].Proceedings of the Vldb Endowment,2016,9(6):492-503.