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Rough set based classification of real world Web services

Author

Listed:
  • Hala S. Own

    (National Research Institute of Astronomy and Geophysics)

  • Hamdi Yahyaoui

    (Kuwait University)

Abstract

We propose in this paper a rough set based classification technique for real world Web services. The set of Web service quality attributes is reduced to a core set, which represents the most relevant set of attributes. Such reduction is achieved through the analysis of the QWS dataset using rough set theory. The core set is then used during the training phase to derive a minimal set of decision rules. These rules are applied during the classification phase to classify real world Web services into one of four categories: Platinum, Gold, Silver, and Bronze. Our experimental results show that the use of rough set theory to classify QWS services allows to have better classification accuracy results compared to other classification techniques, which have been recently applied on QWS.

Suggested Citation

  • Hala S. Own & Hamdi Yahyaoui, 2015. "Rough set based classification of real world Web services," Information Systems Frontiers, Springer, vol. 17(6), pages 1301-1311, December.
    • Handle: RePEc:spr:infosf:v:17:y:2015:i:6:d:10.1007_s10796-014-9496-3
    DOI: 10.1007/s10796-014-9496-3
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    Cited by:

    1. Martin Garriga & Alan De Renzis & Ignacio Lizarralde & Andres Flores & Cristian Mateos & Alejandra Cechich & Alejandro Zunino, 2018. "A structural-semantic web service selection approach to improve retrievability of web services," Information Systems Frontiers, Springer, vol. 20(6), pages 1319-1344, December.
    2. Martin Garriga & Alan De Renzis & Ignacio Lizarralde & Andres Flores & Cristian Mateos & Alejandra Cechich & Alejandro Zunino, 0. "A structural-semantic web service selection approach to improve retrievability of web services," Information Systems Frontiers, Springer, vol. 0, pages 1-26.

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