SS-11: Fuzzy and Rough Hybridization

Neil Mac Parthal´ain, Richard Jensen and Qiang Shen

Rough set theory (RST) has become a topic of great interest to researchers in recent years and has been applied to a variety of different domains (e.g. classification, systems monitoring, information retrieval, clustering, etc.). The reason for this popularity stems from a number of appealing aspects of the theory. Indeed, the focus of RST on grouping information entities into granules in terms of some form of relatedness, offers a certain universal intuitive appeal. However, as RST handles only one type of imperfection found in crisp/nominal data, hybridizations with other soft computing techniques that are tolerant of imperfect data and knowledge such as fuzzy sets offer an improved approach to dealing with additional aspects of data imperfection. Such developments offer a high degree of extensibility and provide robust solutions and advanced tools for data analysis.

Objectives and Topics:
This session is proposed as a forum to:

  • Draw together current original research in the fast-growing area of fuzzy and rough set hybridization.
  • Promote the practical and theoretical extensions of fuzzy and rough hybridizations.
  • Foster the integration of fuzzy and rough hybridizations with other computational intelligence techniques.
  • Novel practical or theoretical contributions describing advances and results are welcomed in all areas where fuzzy and rough set theory can be integrated.

In case you have any question, please do not hesitate to contact the session organizers:

Neil Mac Parthal´ain <>
Richard Jensen <>
Qiang Shen <>



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Electronics and Communication Sciences Unit, Indian Statstical Institute, Kolkata