| dc.description.abstract | Decision tree methods used for extracting information about dataset in
classification problems, although unable to deal with uncertainties embedded
within the data associated with human thinking and perception. This paper
describes the development of a tree induction algorithm which improves the
classification accuracy of decision tree induction. The research is designed by
integrating the principles of CART (Classification and Regression Tree) algorithm
and the fuzzy set-theoretic concepts, enabling the model to handle uncertain and
imprecise data, and in order to soften the sharp decision boundaries which are
inherent in crisp decision tree algorithms. CART is a decision tree algorithm with
the main feature of Gini Index testing (homogenity index) at each level, leading to
the production of trees with pruning process to find the optimal tree. The
application of fuzzy logic to CART decision trees can represent classification
knowledge more naturally and inline with human thinking, and are more robust
when it comes to handling imprecise information. The results of applying fuzzy
logic to CART decision trees are presented in this paper. These have been
obtained from sets of real data, and show that the new fuzzy inference algorithm
improves the accuracy over crisp CART trees. | en_US |
