Title :
Genetic algorithm and fuzzy C-means based multi-voting classification scheme in data mining
Author :
Ou, Mingwen ; Chen, Yubao ; Orady, Elsayed
Author_Institution :
Dept. of Ind. & Manuf. Syst. Eng., Univ. of Michigan-Dearborn, Dearborn, MI, USA
Abstract :
This paper presents a practical scheme used in data mining for classifications based on fuzzy logic and multivoting decision algorithms. It combines the information gain heuristic and genetic algorithm (GA) to minimize the uncertainty level when estimating the weighting functions used in the multiple voting decision scheme. A preliminary test of this scheme using a well-know data set demonstrated its competency and performance improvement for classifications.
Keywords :
data mining; fuzzy logic; genetic algorithms; pattern classification; data mining; fuzzy C-means; fuzzy logic; genetic algorithm; information gain heuristic; multiple voting decision scheme; multivoting classification; multivoting decision algorithm; weighting function estimation; Artificial neural networks; Brain modeling; Data mining; Databases; Decision trees; Genetic algorithms; Mathematical model; Neodymium; Statistics; Uncertainty;
Conference_Titel :
Fuzzy Information Processing Society, 2005. NAFIPS 2005. Annual Meeting of the North American
Print_ISBN :
0-7803-9187-X
DOI :
10.1109/NAFIPS.2005.1548537
