BLEM2: learning Bayes' rules from examples using rough sets
Chien-Chung Chan, S. Sengottiyan · 2004
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.