… We propose here to extend the decision trees method to the case when the involved … fuzzy
sets. First we discuss the reasons why such an extension is to be aimed at. Then in the fuzzy …

… fuzzy decision tree differs from traditional decision trees in two respects: it uses splitting criteria
based on fuzzy … Fuzzy sets defining the fuzzy terms used for building the tree are imposed …

… -scale fuzzy decision tree. Centering on this optimal problem, this paper discusses the
representation of fuzzy decision trees and gives a comparison between fuzzy decision trees and …

… A fuzzy decision tree induction method, which is based on the reduction of classification …
a fuzzy decision tree in the process of reducing classification ambiguity with accumulated fuzzy …

… , fuzzy decision trees have almost exclusively been used for the performance task of classification.
In this paper, we show that a fuzzy extension of decision trees … fuzzy decision trees to …

… the attributes at the decision nodes. We have … decision tree learning algorithm implemented
in the MLLib library and the Chi-FRBCS-BigData algorithm, a MapReduce distributed fuzzy …

… motivations that guides this investigation of fuzzy decision trees. In the present work… decision
trees in order to allow the possibility of partial memberships in the nodes of the decision tree…

… of a fuzzy decision tree based on fuzzy rules – fuzzy rule based decision tree (FRDT) and
provides a learning algorithm. In contrast with “traditional” axis-parallel decision trees in which …

… Therefore, fuzzy sets have been integrated to the decision trees … algorithms to construct fuzzy
decision trees are discussed. … Well-known crisp decision tree induction algorithms from the …

… cluster–based decision tree develops around fuzzy clusters that are treated as generic
building blocks of the tree. The … We treat a C-decision tree as a regular tree structure of the form …