Résumé: 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.

Edition: Alger: CERIST
Langue: Anglais
Collation: 21 p. ;30 cm
Index décimal 621 .Physique appliquée (électrotechnique, génie civil, génie mécanique, ingénierie appliquée, principes physiques en ingénierie)
Thème Informatique

Mots clés:
Feature selection
SLS (stochastic local serach)
SVM (support vector machine)
WSDL
Classification
Web service
Optimization
Meta-heuristic

Note: URl: http://dl.cerist.dz/handle/CERIST/901