DocumentCode
554045
Title
New fuzzy k-NN classification by using genetic algorithm
Author
Junli Lu ; Guang Zhao ; Cheng Yang ; Junjia Lu
Author_Institution
Dept. of Math. & Comput. Sci., Yunnan Univ. of Nat., Kunming, China
Volume
2
fYear
2011
fDate
26-28 July 2011
Firstpage
1111
Lastpage
1115
Abstract
Fuzzy k-NN classification is well-known in data mining, and genetic algorithm is ever been applied to calculate the parameter k and m of fuzzy k-NN, named IFKNN. This paper proposes a new fuzzy k-NN classification method by using genetic algorithm(NFKNN), which need less time and increases classification correct rate. We have verified the efficiency of our methods by theoretical analysis and experiments. The experiments are extensive and comprehensive, we compared each improvement with IFKNN, and we also executed the NFKNN on real datasets and obtained the useful results.
Keywords
data mining; fuzzy set theory; genetic algorithms; pattern classification; NFKNN; data mining; genetic algorithm; new fuzzy k-NN classification method; Accuracy; Biological cells; Classification algorithms; Databases; Genetic algorithms; Glass; Training; fuzzy k-NN; genetic algorithm; sample-edge-of-class; sample-inside-class;
fLanguage
English
Publisher
ieee
Conference_Titel
Natural Computation (ICNC), 2011 Seventh International Conference on
Conference_Location
Shanghai
ISSN
2157-9555
Print_ISBN
978-1-4244-9950-2
Type
conf
DOI
10.1109/ICNC.2011.6022182
Filename
6022182
Link To Document