A new framework for incremental rule induction based on rough sets

2011 ◽  
Author(s):  
Shusaku Tsumoto
Keyword(s):  
Rough Sets ◽  
Rule Induction ◽  
New Framework

Rough Sets Based Rule Generation from Data with Categorical and Numerical Values

2008 ◽  
Vol 12 (5) ◽  
pp. 426-434 ◽  
Author(s):  
Hiroshi Sakai ◽  
◽  
Kazuhiro Koba ◽  
Michinori Nakata ◽  
Keyword(s):  
Machine Learning ◽  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Rough Set Theory ◽  
Rule Generation ◽  
Utility Program ◽  
New Framework

Rough set theory has been mainly applied to data with categorical values. In order to handle data with numerical values in this theory, a familiar concept of ‘wildcards’ was employed, and a new framework of rough sets based rule generation has been proposed. Two characters @ and # were introduced into this framework, and numerical patterns were also defined for numerical values. The concepts of ‘coarse’ and ‘fine’ for rules were explicitly defined according to numerical patterns. This paper enhances the previous framework, and describes the implementation of an utility program. This utility program is applied to the data in UCI Machine Learning Repository, and some useful rules are obtained.

scholarly journals Rough Sets and Rule Induction in Imperfect Information Systems

2014 ◽  
Vol 89 (5) ◽  
pp. 1-8 ◽  
Author(s):  
Do VanNguyen ◽  
Koichi Yamada ◽  
Muneyuki Unehara
Keyword(s):  
Information Systems ◽  
Rough Sets ◽  
Imperfect Information ◽  
Rule Induction
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