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Fuzzy-Neural Inference in Decision Trees

  • Keeley Crockett
  • Zuhair Bandar
  • James O’Shea
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2412)

Abstract

The predominate weakness in the creation of decision trees is the strict partitions which are selected by the induction algorithm. To overcome this problem the theories of fuzzy logic have been applied to generate soft thresholds leading to the creation of fuzzy decision trees, thus allowing cases passing through the tree for classification to be assigned partial memberships down all paths. A challenging task is how these resultant membership grades are combined to produce an overall outcome. A number of theoretical fuzzy inference techniques exist, yet they have not been applied extensively in practical situations and are often domain dependent. Thus the overall classification success of the fuzzy trees has a high dependency on the optimization of the strength of the fuzzy intersection and union operators that are applied. This paper investigates a new, more general approach to combining membership grades using neural-fuzzy inference. Comparisons are made between using the fuzzy-neural approach and the use of pure fuzzy inference trees.

Keywords

Decision Tree Membership Grade Induction Algorithm Fuzzy Decision Tree Fuzzy Intersection 
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 2002

Authors and Affiliations

  • Keeley Crockett
    • 1
  • Zuhair Bandar
    • 1
  • James O’Shea
    • 1
  1. 1.The Intelligent Systems Group, Department Of ComputingThe Manchester Metropolitan UniversityManchesterUK

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