Quality Assessment of Web Services Using Multivariate Adaptive Regression Splines

Abstract

The need to chose a suitable web service in the present scenario, due to the high growth in number of web services that provide similar types of functionalities is a critical task. To select a suitable web service, quality of service (QoS) parameters are efficient to use. In this paper, nine parameters of QoS have been considered as input for design a model using multivariate adaptive regression splines (MARS) to select suitable web service. The performance parameters of MARS model are evaluated and compared with those obtained using models such as: Multivariate Linear Regression, Multivariate Polynomial Regression, Naives Bayes Classifier, Artificial Neural Network. It is observed that the proposed model designed using MARS technique achieved better results as compared to the other three techniques. This paper also focuses on the effectiveness of feature selection techniques to find a small subset of QoS parameters. These may be able to classify the web services with higher accuracy and also reduced the value of misclassification errors.