Automated fuzzy model generation through weight and fuzzification parameters' optimization

this paper we explore the use of weights in the generation of fuzzy models. We automatically generate a fuzzy model, using a three-stage methodology: (i) generation of a crisp model from a decision tree, induced from the data, (ii) transformation of the crisp model into a fuzzy one, and (iii) optimization of the fuzzy model's parameters. Based on this methodology, the generated fuzzy model includes a set of parameters, which are all the parameters included in the sigmoid functions. In addition, local, global and class weights are included, thus the fuzzy model is optimized with respect to both sigmoid function parameters and weights. The class weight introduction, which is a novel approach, grants to the fuzzy model the ability to identify the individual importance of each class and thus more accurately reflect the underlying properties of the classes under examination, in the domain of application. The above described methodology is applied to five known classification problems, obtained from the UCI machine learning repository, and the obtained classification accuracy is high.