Title :
BLEM2: learning Bayes´ rules from examples using rough sets
Author :
Chan, Chien-Chung ; Sengottiyan, Santhosh
Author_Institution :
Dept. of Comput. Sci., Akron Univ., OH, USA
Abstract :
This paper introduces an algorithm for learning Bayes´ rules from examples using rough sets. Induced rules are associated with properties of support, certainty, strength, and coverage factors as defined by Pawlak in his study of connections between rough set theory and Bayes´ theorem. Differences between the two learning algorithms LEM2 and BLEM2 are presented. An idea of how to develop an optimized inference engine by taking advantage of induced rule properties is discussed.
Keywords :
Bayes methods; inference mechanisms; learning (artificial intelligence); optimisation; rough set theory; BLEM2 algorithm; Bayes theorem; LEM2 algorithm; induced rule property; learning algorithm; optimized inference engine; rough set theory; rule certainty; rule coverage factor; rule strength; rule support; Computer science; Engines; Inference algorithms; Production; Rough sets; Set theory; Tellurium;
Conference_Titel :
Fuzzy Information Processing Society, 2003. NAFIPS 2003. 22nd International Conference of the North American
Print_ISBN :
0-7803-7918-7
DOI :
10.1109/NAFIPS.2003.1226779
