Application of the Fuzzy Min-max Neural Network Classifier to Problems with Continuous and Discrete Attributes

The fuzzy min-max classiication network constitutes a promisimg pattern recognition approach that is based on hy-berbox fuzzy sets and can be incrementally trained requiring only one pass through the training set. The deenition and operation of the model considers only attributes assuming continuous values. Therefore, the application of the fuzzy min-max network to a problem with continous and discrete attributes, requires the modiication of its deenition and operation in order to deal with the discrete dimensions. Experimental results using the modiied model on a diicult pattern recognition problem establishes the strengths and weaknesses of the proposed approach.