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

fYear

2003

fDate

24-26 July 2003

Firstpage

187

Lastpage

190

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Information Processing Society, 2003. NAFIPS 2003. 22nd International Conference of the North American

Print_ISBN

0-7803-7918-7

Type

conf

DOI

10.1109/NAFIPS.2003.1226779

Filename

1226779

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1978245