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
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;
Conference_Titel :
IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-7078-3
DOI :
10.1109/NAFIPS.2001.944697
