DocumentCode
2067662
Title
Classification rule mining research based on hybrid genetic algorithm
Author
Wang Xiangrui ; Wang Shuai
Author_Institution
Sch. of Comput. Sci. & Eng., Jilin Inst. of Archit. & Civil Eng., Changchun, China
fYear
2011
fDate
16-18 Dec. 2011
Firstpage
292
Lastpage
294
Abstract
Traditional classification rule mining based on genetic algorithm usually exists the problem of low quality of the mined rules, too many redundant rules in the population after optimization, and inaccuracy in classification. The paper analyzes the principles of classification rules mining, and proposes that classification rules mining methods based on hybrid genetic algorithm can effectively overcome the above disadvantages and improve the accuracy of classification rule mining.
Keywords
data mining; genetic algorithms; pattern classification; classification rule mining research; hybrid genetic algorithm; mined rules; optimization; redundant rules; traditional classification rule mining; Accuracy; Biological cells; Classification algorithms; Data mining; Genetic algorithms; Genetics; Training; Classification Rules; Data Mining; Genetic Algorithm; Hybrid Genetic Algorithms; Local Search;
fLanguage
English
Publisher
ieee
Conference_Titel
Transportation, Mechanical, and Electrical Engineering (TMEE), 2011 International Conference on
Conference_Location
Changchun
Print_ISBN
978-1-4577-1700-0
Type
conf
DOI
10.1109/TMEE.2011.6199200
Filename
6199200
Link To Document