An algorithm for generating fuzzy decision tree with trapezoid fuzzy number-value attributes
Dongmei Huang · 2008
This paper presented an algorithm based on the L-R-COG using the information entropy minimization heuristic for generating decision tree with the trapezoid fuzzy number-value attributes. We first define the L-R-center of gravity ( L-R-COG(Ã) ) of the trapezoid fuzzy number-value attribute à = (a,b, c, d), and next compute its L-R-COG(Ã), in the end we use the information entropy Enty(Ã,T,S)minimization heuristic to choose the test attribute for generating decision tree. By considering the L-R-COG(Ã)and analyzing non-stable points, the presented algorithm gives us a desirable behavior of the information entropy of partitioning. Finally, an example shows the utility of the proposed algorithm. Specially, if d = c = a = b to à = (a,b, c, d), the corresponding learning algorithm is the learning algorithm of decision tree generation for continuous-valued attributes [2].