Web service classification based on non-functional parameters using vote based classifier

Abstract

Today the internet has become a very vital part of everyone’s life. Nowadays, Companies are offering their business in the form of web services to reach their customers. Web services help in business by providing a standard way to communicate with their customers. But the rapid introduction of modern web services in this dynamic business environment influences the service quality and customer satisfaction. So, there is a requirement to focus on the functional as well as the non-functional aspect of web services. In this paper, for accurately classifying the web services vote based classifier has been used. For classification J48, Naive Bayes, SVM and Random Forest are used as a base learner. The proposed vote based classifier model provides accurate classification and prediction by improving the recall and precision value as compared to individual classifiers. The experiment is conducted on a real dataset of web services to find out the accurate result. The results show that the vote based classifier performs better in comparison to others. Experimental outcomes show that the proposed approach determines the most promising results.

Subject Classification:

• 68N19

Keywords:

• Web Services
• Quality of Service (QoS)
• Multi-Criteria Based Decision Making (MCDM)
• WSDL (Web Services Description Language)
• XML (Extensible Mark-up Language)