DocumentCode
1750949
Title
Methods of learning rules based on rough set: LBR and LEM3
Author
Shu, Lan ; Wen, Mo Zhi ; Dan, Hu
Author_Institution
Dept. of Appl. Math., Univ. of Electron. Sci. & Technol., Chengdu, China
Volume
2
fYear
2001
fDate
25-28 July 2001
Firstpage
753
Abstract
With the help of rough set theory, this paper puts forward a new way of machine learning - LBR (Learning By Rough set theory) - and then compares it with the algorithm LEM1 (Learning from Examples Method 1) that was proposed in "Incomplete Information Rough Set Analysis", Physica-Verlag Heidelberg, Ewa Orlowska (Ed.), 1998. From the comparison results, we find a new method of learning rules from examples, named LEM3, which is more flexible than LEM1. LBR and LEM3 have extensive application prospects in artificial intelligence
Keywords
learning by example; rough set theory; LBR algorithm; LEM1 algorithm; LEM3 algorithm; artificial intelligence; decision table; fuzzy-rough sets; incomplete information; learning from examples; machine learning; rough set theory; Artificial intelligence; Bismuth; Computer science; Machine learning; Mathematics; Rough sets; Set theory; Software algorithms;
fLanguage
English
Publisher
ieee
Conference_Titel
IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th
Conference_Location
Vancouver, BC
Print_ISBN
0-7803-7078-3
Type
conf
DOI
10.1109/NAFIPS.2001.944697
Filename
944697
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