Learning fuzzy classification rules from labeled data
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on recent advances in soft computing
Fuzzy support vector machine for multi-class text categorization
Information Processing and Management: an International Journal
Fuzzy functions with support vector machines
Information Sciences: an International Journal
On support vector regression machines with linguistic interpretation of the kernel matrix
Fuzzy Sets and Systems
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
Similarity measures in fuzzy rule base simplification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Simplifying fuzzy rule-based models using orthogonal transformationmethods
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Semantic constraints for membership function optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement
IEEE Transactions on Fuzzy Systems
GA-fuzzy modeling and classification: complexity and performance
IEEE Transactions on Fuzzy Systems
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
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A new approach is proposed for the data-based identification of transparent fuzzy rule-based classifiers. It is observed that fuzzy rule-based classifiers work in a similar manner as kernel function-based support vector machines (SVMs) since both model the input space by nonlinearly maps into a feature space where the decision can be easily made. Accordingly, trained SVM can be used for the construction of fuzzy rule-based classifiers. However, the transformed SVM does not automatically result in an interpretable fuzzy model because the SVM results in a complex rule-base, where the number of rules is approximately 40-60% of the number of the training data. Hence, reduction of the SVM-initialized classifier is an essential task. For this purpose, a three-step reduction algorithm is developed based on the combination of previously published model reduction techniques. In the first step, the identification of the SVM is followed by the application of the Reduced Set method to decrease the number of kernel functions. The reduced SVM is then transformed into a fuzzy rule-based classifier. The interpretability of a fuzzy model highly depends on the distribution of the membership functions. Hence, the second reduction step is achieved by merging similar fuzzy sets based on a similarity measure. Finally, in the third step, an orthogonal least-squares method is used to reduce the number of rules and re-estimate the consequent parameters of the fuzzy rule-based classifier. The proposed approach is applied for the Wisconsin Breast Cancer, Iris and Wine classification problems to compare its performance to other methods.