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A Rough Fuzzy Perspective to Dimensionality Reduction

  • Alessio FeroneEmail author
  • Alfredo Petrosino
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7627)

Abstract

Rough set theory and fuzzy logic are mathematical frameworks for granular computing forming a theoretical basis for the treatment of uncertainty in many real–world problems. The focus of rough set theory is on the ambiguity caused by limited discernibility of objects in the domain of discourse; granules are formed as objects and are drawn together by the limited discernibility among them. On the other hand, membership functions of fuzzy sets enables efficient handling of overlapping classes. The hybrid notion of rough fuzzy sets comes from the combination of these two models of uncertainty and helps to exploit, at the same time, properties like coarseness and vagueness. We describe a model of the hybridization of rough and fuzzy sets, that allows for further refinements of rough fuzzy sets and show its application to the task of unsupervised feature selection.

Keywords

Rough fuzzy sets Modelling hierarchies Unsupervised feature selection 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Department of Science and TechnologyUniversity of Naples ParthenopeNaplesItaly

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