Probabilistic fuzzy systems make it possible to model linguistic uncertainty and probabilistic uncertainty in a single system. This paper is concerned with the estimation of the parameters in probabilistic fuzzy classifiers. The purpose of the paper is to introduce a new method that simultaneously estimates all the parameters in a probabilistic fuzzy classifier. The method uses a maximum likelihood criterion and a gradient-based optimization algorithm. The performance of the method is evaluated on two benchmark data sets. The method is compared with a sequential parameter estimation method used in previous publications. Also, a comparison with an alternative method from the literature is made.
|Title of host publication||The 14th IEEE International Conference on Fuzzy Systems (FUZZ '05), 25 May 2005, Reno|
|Place of Publication||Piscataway|
|Publisher||Institute of Electrical and Electronics Engineers|
|Publication status||Published - 2005|