C4.5: programs for machine learning
C4.5: programs for machine learning
Lipschitzian optimization without the Lipschitz constant
Journal of Optimization Theory and Applications
Induction of fuzzy decision trees
Fuzzy Sets and Systems
Fuzzy Sets and Systems - Special issue on fuzzy optimization
An algorithmic framework for development and optimization of fuzzy models
Fuzzy Sets and Systems
A course in fuzzy systems and control
A course in fuzzy systems and control
Weighted fuzzy production rules
Fuzzy Sets and Systems
FILM: a fuzzy inductive learning method for automated knowledge acquisition
Decision Support Systems - Special issue: expertise and modeling expert decision making
Neural Networks
Globally Optimal Fuzzy Decision Trees for Classification and Regression
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the optimization of fuzzy decision trees
Fuzzy Sets and Systems
Fuzzy and Neural Approaches in Engineering
Fuzzy and Neural Approaches in Engineering
Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
SIAM Journal on Optimization
A complete fuzzy decision tree technique
Fuzzy Sets and Systems - Theme: Learning and modeling
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Fuzzy decision trees: issues and methods
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Genetically optimized fuzzy decision trees
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Approximation accuracy analysis of fuzzy systems as function approximators
IEEE Transactions on Fuzzy Systems
Application of simulated annealing fuzzy model tuning to umbilical cord acid-base interpretation
IEEE Transactions on Fuzzy Systems
Improving learning accuracy of fuzzy decision trees by hybrid neural networks
IEEE Transactions on Fuzzy Systems
Logic-based fuzzy networks: A study in system modeling with triangular norms and uninorms
Fuzzy Sets and Systems
CART data analysis to attain interpretability in a fuzzy logic classifier
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Designing simulated annealing and subtractive clustering based fuzzy classifier
Applied Soft Computing
Data driven generation of fuzzy systems: an application to breast cancer detection
CIBB'10 Proceedings of the 7th international conference on Computational intelligence methods for bioinformatics and biostatistics
Design of fuzzy radial basis function-based polynomial neural networks
Fuzzy Sets and Systems
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In this paper we propose a generic methodology for the automated generation of fuzzy models. The methodology is realized in three stages. Initially, a crisp model is created and in the second stage it is transformed to a fuzzy one. In the third stage, all parameters entering the fuzzy model are optimized. The proposed methodology is novel and generic since it can integrate alternative techniques in each of its stages. A specific realization of this methodology is implemented, using decision trees for the creation of the crisp model, the sigmoid function, the min-max operators and the maximum defuzzifier, for the transformation of the crisp model into a fuzzy one, and four different optimization strategies, including global and local optimization techniques, as well as, hybrid approaches. The proposed methodology presents several advantages and novelties: the transformation of the crisp model to the respective fuzzy one is straightforward ensuring its full automated nature and it introduces a set of parameters, expressing the importance of each fuzzy rule. The above realization is extensively evaluated using several benchmark data sets from the UCI machine learning repository and the obtained results indicate its high efficiency.