Interval-valued fuzzy decision trees

This research proposes a new model for constructing decision trees using interval-valued fuzzy membership values employing on look-ahead based fuzzy decision tree induction and interval-valued fuzzy sets. Most existing fuzzy decision trees do not consider the uncertainty associated with their membership values. However, precise values of fuzzy membership values are not always possible. In this paper, we represent fuzzy membership values as intervals to model uncertainty and employ the look-ahead based fuzzy decision tree induction method and Hamming distance of interval-valued fuzzy sets to construct decision trees. An example is given to demonstrate the effectiveness of the approach.