An algorithm for generating fuzzy decision tree based on center of gravity

The center of gravity (COG) of fuzzy sets is an essential feature that concurrently reflects the location and shape of the fuzzy sets concerned, this paper treated the COG as the core of any fuzzy membership function, and presented an algorithm based on the COG using the information entropy minimization heuristic for generating decision tree with fuzzy value attributes. By considering the center of gravity (COG) of the fuzzy value attribute and analyzing non-stable partition points, the presented algorithm gives us a desirable behavior of the information entropy of partitioning. To the unknown-classified sample data, the algorithm offers a rapid matching speed. Finally, the example on medical records that we collected in a hospital shows the utility of the proposed algorithm. Comparison to the heuristic algorithm 1[7], the presented algorithm based on COG the has a stronger generalizing ability.