Title :
Knowledge pruning in decision trees
Author :
Shioya, Isamu ; Miura, Takao
Author_Institution :
Sanno Univ., Kanagawa, Japan
Abstract :
We propose a novel pruning method of decision trees based on domain knowledge, semantic hierarchies among classes, which is used to generate decision trees by relaxing the levels of hierarchies for both height and width of the trees. We develop the algorithm, and the effectiveness is examined by UCI Machine Learning Repository: On Car Evaluation and Nursery. We can generate the decision trees consisting of 11 and 13 rules, although C4.5 generates 182 and 572 rules, respectively
Keywords :
data mining; decision trees; learning (artificial intelligence); UCI Machine Learning Repository On Car Evaluation and Nursery; decision trees; domain knowledge; hierarchy levels; knowledge pruning; pruning method; semantic hierarchies; Classification tree analysis; Data mining; Decision trees; Entropy; Instruction sets; Machine learning algorithms; Stress; Temperature; Testing; Vehicles;
Conference_Titel :
Tools with Artificial Intelligence, 2000. ICTAI 2000. Proceedings. 12th IEEE International Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7695-0909-6
DOI :
10.1109/TAI.2000.889844
