Advertisement

Using Fuzzy Decision Tree to Handle Uncertainty in Context Deduction

  • Donghai Guan
  • Weiwei Yuan
  • A. Gavrilov
  • Sungyoung Lee
  • Youngkoo Lee
  • Sangman Han
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4114)

Abstract

In context-aware systems, one of the main challenges is how to tackle context uncertainty well, since perceived context always yields uncertainty and ambiguity with consequential effect on the performance of context-aware systems. We argue that uncertainty is mainly generated by two sources. One is sensor’s inherent inaccuracy and unreliability. The other source is deduction process from low-level context to high-level context. Decision tree is an appropriate candidate for reasoning. Its distinct merit is that once a decision tree has been constructed, it is simple to convert it into a set of human-understandable rules. So human can easily improve these rules. However, one inherent disadvantage of decision tree is that the use of crisp points makes the decision trees sensitive to noise. To overcome this problem, we propose an alternative method, fuzzy decision tree, based on fuzzy set theory.

Keywords

Decision Tree Membership Function Bayesian Network Information Gain Pervasive Computing 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Donghai Guan
    • 1
  • Weiwei Yuan
    • 1
  • A. Gavrilov
    • 1
  • Sungyoung Lee
    • 1
  • Youngkoo Lee
    • 1
  • Sangman Han
    • 1
  1. 1.Department of Computer Engineering, Kyung Hee UniversityKorea

Personalised recommendations