Using Fuzzy Decision Tree to Handle Uncertainty in Context Deduction

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.