A Majority Vote Based Classifier Ensemble for Web Service Classification

Abstract

Service oriented architecture is a glue that allows web applications to work in collaboration. It has become a driving force for the service-oriented computing (SOC) paradigm. In heterogeneous environments the SOC paradigm uses web services as the basic building block to support low costs as well as easy and rapid composition of distributed applications. A web service exposes its interfaces using the Web Service Description Language (WSDL). A central repository called universal description, discovery and integration (UDDI) is used by service providers to publish and register their web services. UDDI registries are used by web service consumers to locate the web services they require and metadata associated with them. Manually analyzing WSDL documents is the best approach, but also most expensive. Work has been done on employing various approaches to automate the classification of web services. However, previous research has focused on using a single technique for classification. This research paper focuses on the classification of web services using a majority vote based classifier ensemble technique. The ensemble model overcomes the limitations of conventional techniques by employing the ensemble of three heterogeneous classifiers: Naïve Bayes, decision tree (J48), and Support Vector Machines. We applied tenfold cross-validation to test the efficiency of the model on a publicly available dataset consisting of 3738 real world web services categorized into 5 fields, which yielded an average accuracy of 92 %. The high accuracy is owed to two main factors, i.e., enhanced pre-processing with focused feature selection, and majority based ensemble classification.