A neurofuzzy classifier for two class problems

Author

Gao, Ming ; Hong, Xia ; Harris, Chris J.

Author_Institution

Sch. of Syst. Eng., Univ. of Reading, Reading, UK

fYear

2012

fDate

5-7 Sept. 2012

Firstpage

1

Lastpage

6

Abstract

A neurofuzzy classifier identification algorithm is introduced for two class problems. The initial fuzzy base construction is based on fuzzy clustering utilizing a Gaussian mixture model (GMM) and the analysis of covariance (ANOVA) decomposition. The expectation maximization (EM) algorithm is applied to determine the parameters of the fuzzy membership functions. Then neurofuzzy model is identified via the supervised subspace orthogonal least square (OLS) algorithm. Finally a logistic regression model is applied to produce the class probability. The effectiveness of the proposed neurofuzzy classifier has been demonstrated using a real data set.

Keywords

Gaussian processes; expectation-maximisation algorithm; fuzzy neural nets; fuzzy set theory; least squares approximations; pattern classification; probability; regression analysis; ANOVA decomposition; EM algorithm; GMM; Gaussian mixture model; OLS algorithm; analysis-of-covariance decomposition; class probability; expectation maximization algorithm; fuzzy base construction; fuzzy clustering; fuzzy membership function parameter determination; logistic regression model; neurofuzzy classifier identification algorithm; supervised subspace orthogonal least square algorithm; two-class problems; Analysis of variance; Clustering algorithms; Data models; Fuzzy logic; Logistics; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence (UKCI), 2012 12th UK Workshop on

Conference_Location

Edinburgh

Print_ISBN

978-1-4673-4391-6

Type

conf

DOI

10.1109/UKCI.2012.6335763

Filename

6335763