A stochastic local search combined with support vector machine for web services classification
In this paper, we are interested in the Web service classification. We propose a classification method that first uses a stochastic local search (SLS) meta-heuristic for feature selection then call the Support Vector Machine (SVM) to do the classification task. The proposed method that combines SLS and SVM for Web service classification is validated on the QWS Dataset to measure its performance. We used a set of 364 Web services divided into four categories (Platinum, Gold, Silver and Bronze) in which quality is measured by 9 attributes. The experiments and the comparison show the effectiveness of our method for the classification of Web services.
Web service, WSDL, classification, SVM (support vector machine), SLS (stochastic local serach), feature selection, optimization, meta-heuristic
ISRN :CERIST- DTRI-16-000000016-DZ
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