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Towards a Fuzzy Extension of the López de Mántaras Distance

  • Eva Armengol
  • Pilar Dellunde
  • Àngel García-Cerdaña
Part of the Communications in Computer and Information Science book series (CCIS, volume 297)

Abstract

In this paper we introduce FLM, a divergence measure to compare a fuzzy and a crisp partition. This measure is an extension of LM, the López de Mántaras distance. This extension allows to handle domain objects having attributes with continuous values. This means that for some domains the use of fuzzy sets may report better results than the discretization that is the usual way to deal with continuous values. We experimented with both FLM and LM in the context of the lazy learning method called Lazy Induction of Descriptions useful for classification tasks.

Keywords

Machine learning partitions fuzzy partitions entropy measures López de Mántaras distance 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Eva Armengol
    • 1
  • Pilar Dellunde
    • 1
    • 2
  • Àngel García-Cerdaña
    • 1
    • 3
  1. 1.Artificial Intelligence Research Institute (IIIA - CSIC)BellaterraSpain
  2. 2.Departament de FilosofiaUniversitat Autònoma de BarcelonaBellaterraSpain
  3. 3.Departament de Lògica, Història i Filosofia de la CiènciaUniversitat de BarcelonaBarcelonaSpain

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