Machine Learning
Neural network design
Nearest neighbor classifier: simultaneous editing and feature selection
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
A genetic feature weighting scheme for pattern recognition
Integrated Computer-Aided Engineering
An efficient gait recognition based on a selective neural network ensemble
International Journal of Imaging Systems and Technology
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Many classification methods have been reported and the most popular ones among them are multilayer perceptron (MLP), nearest neighbor (NN), and support vector machine (SVM), etc. All of them have the weakness that they are not transparent or not clearly understandable to human beings. Sometimes, however, linguistically understandable classifiers could be preferred to the nontransparent models. Especially, when we are given a large set of data and we have to draw concise but interpretable hypothesis or conclusion, linguistically understandable classifiers should be required. In this paper, a linguistically understandable fuzzy classifier is presented and a new training method is proposed. To handle the uncertainties stemming from the problem or the measurement, the fuzzy classifier, the consequent part outputs the degree of truth for the assignment of each fuzzy set to the classes.