A new design method for linguistically understandable fuzzy classifier

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.

[1]  Euntai Kim,et al.  A genetic feature weighting scheme for pattern recognition , 2007, Integr. Comput. Aided Eng..

[2]  V. Kshirsagar,et al.  Face recognition using Eigenfaces , 2011, 2011 3rd International Conference on Computer Research and Development.

[3]  Euntai Kim,et al.  An efficient gait recognition based on a selective neural network ensemble , 2008 .

[4]  Lakhmi C. Jain,et al.  Nearest neighbor classifier: Simultaneous editing and feature selection , 1999, Pattern Recognit. Lett..

[5]  Martin T. Hagan,et al.  Neural network design , 1995 .

[6]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  S. Salzberg,et al.  A weighted nearest neighbor algorithm for learning with symbolic features , 2004, Machine Learning.

[8]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[9]  María José del Jesús,et al.  Some relationships between fuzzy and random set-based classifiers and models , 2002, Int. J. Approx. Reason..

[10]  Lotfi A. Zadeh,et al.  Information Title : Fuzzy languages and their relation to human and machine intelligence , 2022 .

[11]  Sharath Pankanti,et al.  An identity-authentication system using fingerprints , 1997, Proc. IEEE.