Title :
Improving Premise Structure in Evolving Takagi-Sugeno Neuro-Fuzzy Classifiers
Author :
Almaksour, Abdullah ; Anquetil, Eric
Author_Institution :
INSA de Rennes, Rennes, France
Abstract :
We present in this paper a new method for the design of evolving neuro-fuzzy classifiers. The presented approach is based on a first-order Takagi-Sugeno neuro-fuzzy model. We propose a modification on the premise structure in this model and we provide the necessary learning formulas, with no problem-dependent parameters. We demonstrate by the experimental results the positive effect of this modification on the overall classification performance.
Keywords :
learning (artificial intelligence); pattern classification; Takagi-Sugeno neurofuzzy classifiers; first-order Takagi-Sugeno neurofuzzy model; learning formulas; Adaptation model; Clustering algorithms; Covariance matrix; Databases; Prototypes; Takagi-Sugeno model; Tuning; Incremental learning; Takagi-Sugeno; neuro-fuzzy;
Conference_Titel :
Machine Learning and Applications (ICMLA), 2010 Ninth International Conference on
Conference_Location :
Washington, DC
Print_ISBN :
978-1-4244-9211-4
DOI :
10.1109/ICMLA.2010.91
