Abstract

Fuzzy rough sets are widely studied and applied in the domain of machine learning and data mining these years. In this work, this theory is used to design a fuzzy rough decision tree algorithm which can be used to deal with the cognitive uncertainties such as vagueness and ambiguity associated with human thinking and perception. In our algorithm, both selecting nodes and splitting branches in constructing the tree are based on fuzzy rough set theory. Especially, the current branching point is determined by pureness of the two branches, where the pureness is based on fuzzy lower approximation. The comparison results show that our decision tree algorithm is equivalent to or outperforms some popular decision tree algorithms.

Keywords

Decision Tree Decision Tree Algorithm Fuzzy Equivalence Relation Fuzzy Decision Tree Select Node 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Shuang An
    • 1
    • 2
  • Qinghua Hu
    • 2
  1. 1.Northeastern UniversityShenyangP.R. China
  2. 2.Tianjin UniversityTianjinP.R. China

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