DocumentCode
2925084
Title
A new framework for incremental rule induction based on rough sets
Author
Tsumoto, Shusaku
Author_Institution
Dept. of Med. Inf., Shimane Univ., Enya-cho Izumo, Japan
fYear
2011
fDate
8-10 Nov. 2011
Firstpage
681
Lastpage
686
Abstract
This paper proposes a new framework for incremental learning based on accuracy and coverage. Classified addition of example into four cases, two inequalities for accuracy and coverage are obtained. The proposed method classifies a set of formulae into three layers: rule layer, subrule layer and non-rule layer by using the inequalities obtained. Then, subrule layer plays a central role in updating rules.
Keywords
data mining; learning (artificial intelligence); rough set theory; incremental learning; incremental rule induction; nonrule layer; rough sets; rule layer; subrule layer; Accuracy; Computational modeling; Databases; Learning systems; Probabilistic logic; Rough sets; accuracy; coverage; incremental rule induction; rough sets; subrule layer;
fLanguage
English
Publisher
ieee
Conference_Titel
Granular Computing (GrC), 2011 IEEE International Conference on
Conference_Location
Kaohsiung
Print_ISBN
978-1-4577-0372-0
Type
conf
DOI
10.1109/GRC.2011.6122679
Filename
6122679
Link To Document