Title :
A comparative study on heuristic algorithms for generating fuzzy decision trees
Author :
Wang, X.Z. ; Tsang, E.C.C. ; Yeung, D.S.
Author_Institution :
Dept. of Comput., Hong Kong Polytech. Univ., Kowloon, Hong Kong
fDate :
6/21/1905 12:00:00 AM
Abstract :
Fuzzy decision tree induction is an important way of learning from fuzzy examples. Since the construction of an optimal decision tree is NP-hard, heuristic algorithms are necessary. In the paper, three heuristic algorithms for generating fuzzy decision trees have been compared and analyzed. One of them was proposed by the authors. The analytic comparison is based on four issues: expanded attribute selection, leaf-node standard, matching approach and computation complexity. The comparative results have given some important information about heuristics and have shown useful guidelines to choose an appropriate heuristic for a particular problem
Keywords :
computational complexity; decision trees; fuzzy logic; learning by example; NP-hard problem; computation complexity; expanded attribute selection; fuzzy decision tree induction; heuristic algorithms; leaf-node standard; matching approach; optimal decision tree; Algorithm design and analysis; Buildings; Classification tree analysis; Decision trees; Fuzzy sets; Guidelines; Heuristic algorithms; Induction generators; Information analysis; Tree graphs;
Conference_Titel :
Systems, Man, and Cybernetics, 1999. IEEE SMC '99 Conference Proceedings. 1999 IEEE International Conference on
Conference_Location :
Tokyo
Print_ISBN :
0-7803-5731-0
DOI :
10.1109/ICSMC.1999.823227
