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

fYear

2005

fDate

26-28 June 2005

Firstpage

222

Lastpage

227

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Information Processing Society, 2005. NAFIPS 2005. Annual Meeting of the North American

Print_ISBN

0-7803-9187-X

Type

conf

DOI

10.1109/NAFIPS.2005.1548537

Filename

1548537

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2641871