A Soft Discretization Technique for Fuzzy Decision Trees Using Resampling

  • Taimur Qureshi
  • D. A. Zighed
Conference paper

DOI: 10.1007/978-3-642-04394-9_71

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5788)
Cite this paper as:
Qureshi T., Zighed D.A. (2009) A Soft Discretization Technique for Fuzzy Decision Trees Using Resampling. In: Corchado E., Yin H. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2009. IDEAL 2009. Lecture Notes in Computer Science, vol 5788. Springer, Berlin, Heidelberg

Abstract

Decision trees generate classifiers from training data through a process of recursively splitting the data space. In the case of training on continuous-valued data, the associated attributes must be discretized into several intervals using a set of crisp cut points. One drawback of decision trees is their instability, i.e., small data deviations may require a significant reconstruction of the decision tree. Here, we present a novel soft decision tree method that uses soft of fuzzy discretization instead of traditional crisp cuts. We use a resampling based technique to generate soft discretization points and demonstrate the advantages of using our resampling based soft discretization over traditional crisp methods.

Keywords

Crisp discretization resampling fuzzy discretization 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Taimur Qureshi
    • 1
  • D. A. Zighed
    • 1
  1. 1.Laboratory ERICUniversity of Lyon 2Bron CedexFrance

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