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
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;
Conference_Titel :
Natural Computation (ICNC), 2011 Seventh International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-9950-2
DOI :
10.1109/ICNC.2011.6022182