DocumentCode
2309265
Title
An efficient SFL-based classification rule mining algorithm
Author
Yin, Hui ; Cheng, Fengjuan ; Zhou, Chunjie
Author_Institution
Dept. of Control Sci. & Eng., Huazhong Univ. of Sci. & Technol., Wuhan
fYear
2008
fDate
12-14 Dec. 2008
Firstpage
969
Lastpage
972
Abstract
Classification rule mining is an important data mining process that aims to discover a small set of rules from the training data set with predetermined targets. The shuffled frog leaping(SFL) algorithm, is a new robust evolutionary algorithm based on the local search and the shuffling processes. In this paper, an efficient SFL-based classification rule mining algorithm is proposed. The experimental results show that the proposed algorithm performs much better than other related algorithms.
Keywords
data mining; evolutionary computation; pattern classification; SFL-based classification rule mining algorithm; data mining process; robust evolutionary algorithm; shuffled frog leaping algorithm; Classification algorithms; Control engineering education; Data engineering; Data mining; Databases; Educational technology; Evolutionary computation; Genetics; Robustness; Training data;
fLanguage
English
Publisher
ieee
Conference_Titel
IT in Medicine and Education, 2008. ITME 2008. IEEE International Symposium on
Conference_Location
Xiamen
Print_ISBN
978-1-4244-3616-3
Electronic_ISBN
978-1-4244-2511-2
Type
conf
DOI
10.1109/ITME.2008.4744012
Filename
4744012
Link To Document