DocumentCode
3122808
Title
Generation of approximation rules using information gain
Author
Chung, Hong ; Choi, Kyung-Oak ; Chung, Hwan-Mook
Author_Institution
Dept. of Comput. Eng., Keimyung Univ., Taegu, South Korea
Volume
3
fYear
1999
fDate
22-25 Aug. 1999
Firstpage
1241
Abstract
Suggests a method for generating approximation rules by the information gain used in the machine learning by decision tree. We studied that these rules are better than other approximation rules induced by using /spl chi//sup 2/ goodness of fittest or dependency of attributes in rough set theory by an experiment using neural network.
Keywords
decision trees; entropy; learning (artificial intelligence); neural nets; rough set theory; /spl chi//sup 2/ goodness; approximation rules; attributes; decision tree; dependency; information gain; machine learning; rough set theory; Classification tree analysis; Decision trees; Entropy; Lenses; Level set; Machine learning; Machine learning algorithms; Neural networks; Set theory; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems Conference Proceedings, 1999. FUZZ-IEEE '99. 1999 IEEE International
Conference_Location
Seoul, South Korea
ISSN
1098-7584
Print_ISBN
0-7803-5406-0
Type
conf
DOI
10.1109/FUZZY.1999.790079
Filename
790079
Link To Document