Performance Analysis of Ensemble Supervised Machine Learning Algorithms for Missing Value Imputation

Abstract

In this era of cloud computing, web services based solutions are gaining popularity. The applications running on distributed environment seek new parameters for them to perform efficiently to satisfy end user's requirements. Finding these parameters for increasing efficiency has become a talk of researchers now days. Non functional performance of a web service is described through User dependent QoS properties. These QoS parameters are generally described in WS-Policy in Service Level Agreement (SLA). Usually in web service QoS datasets, web service QoS values are missing, which makes missing value imputations an important job while working with cloud web services. In the current work we compared the prediction accuracy of two groups of supervised machine learning ensembles based Meta learners: bagging and additive regression (boosting) with a fusion of the seven base learners in both. Random forest is found to be better performing in both Meta learners: bagging and boosting than other learning algorithms.