DocumentCode
2335546
Title
Efficient splitting rules based on the probabilities of pre-assigned intervals
Author
Cho, June-Suh ; Adam, Nabil R.
Author_Institution
IBM T.J. Watson Res. Center, Hawthorne, NY, USA
fYear
2001
fDate
2001
Firstpage
584
Lastpage
585
Abstract
The paper describes novel methods for classification in order to find an optimal tree. Unlike the current splitting rules that are provided by searching all threshold values, the paper proposes splitting rules that are based on the probabilities of pre-assigned intervals. In experiments, we demonstrate that our methods properly classify image objects based on new split rules
Keywords
image classification; knowledge based systems; optimisation; probability; tree searching; classification methods; computational complexity; image object classification; optimal cutoff point; optimal tree; pre-assigned interval probabilities; split rules; splitting rules; threshold values; Error analysis; Milling machines; Radio access networks; Rivers; Search methods;
fLanguage
English
Publisher
ieee
Conference_Titel
Data Mining, 2001. ICDM 2001, Proceedings IEEE International Conference on
Conference_Location
San Jose, CA
Print_ISBN
0-7695-1119-8
Type
conf
DOI
10.1109/ICDM.2001.989570
Filename
989570
Link To Document