DocumentCode
2704670
Title
Knowledge pruning in decision trees
Author
Shioya, Isamu ; Miura, Takao
Author_Institution
Sanno Univ., Kanagawa, Japan
fYear
2000
fDate
2000
Firstpage
40
Lastpage
43
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Tools with Artificial Intelligence, 2000. ICTAI 2000. Proceedings. 12th IEEE International Conference on
Conference_Location
Vancouver, BC
ISSN
1082-3409
Print_ISBN
0-7695-0909-6
Type
conf
DOI
10.1109/TAI.2000.889844
Filename
889844
Link To Document