A decision theoretic approach to hierarchical classifier design
Pattern Recognition
Large Tree Classifier with Heuristic Search and Global Training
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to the theory of neural computation
Introduction to the theory of neural computation
An Iterative Growing and Pruning Algorithm for Classification Tree Design
IEEE Transactions on Pattern Analysis and Machine Intelligence
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
C4.5: programs for machine learning
C4.5: programs for machine learning
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
A Comparative Analysis of Methods for Pruning Decision Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning by symbolic and neural methods
The handbook of brain theory and neural networks
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Machine Learning
Fuzzy decision trees: issues and methods
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Comparison of adaptive methods for function estimation from samples
IEEE Transactions on Neural Networks
Backpropagation in Decision Trees for Regression
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Fuzzy classification trees for data analysis
Fuzzy Sets and Systems
A complete fuzzy decision tree technique
Fuzzy Sets and Systems - Theme: Learning and modeling
Comparative Assessment of Software Quality Classification Techniques: An Empirical Case Study
Empirical Software Engineering
Resource-oriented software quality classification models
Journal of Systems and Software
A fuzzy decision tree-based duration model for Standard Yorùbá text-to-speech synthesis
Computer Speech and Language
Software quality estimation with limited fault data: a semi-supervised learning perspective
Software Quality Control
Tree-structured smooth transition regression models
Computational Statistics & Data Analysis
A methodology for automated fuzzy model generation
Fuzzy Sets and Systems
Elgasir: an algorithm for creating fuzzy regression trees
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Optimization of fuzzy rules for classification using genetic algorithm
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Hierarchical mixtures of autoregressive models for time-series modeling
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
A neural network of smooth hinge functions
IEEE Transactions on Neural Networks
EDLRT: Entropy-based dummy variables logistic regression tree
Intelligent Data Analysis
Optimized fuzzy decision tree using genetic algorithm
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Feature set search space for fuzzyboost learning
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Optimized fuzzy classification using genetic algorithm
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
Fuzzy fast classification algorithm with hybrid of ID3 and SVM
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Recent Advances in Soft Computing: Theories and Applications
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A fuzzy decision tree is constructed by allowing the possibility of partial membership of a point in the nodes that make up the tree structure. This extension of its expressive capabilities transforms the decision tree into a powerful functional approximant that incorporates features of connectionist methods, while remaining easily interpretable. Fuzzification is achieved by superimposing a fuzzy structure over the skeleton of a CART decision tree. A training rule for fuzzy trees, similar to backpropagation in neural networks, is designed. This rule corresponds to a global optimization algorithm that fixes the parameters of the fuzzy splits. The method developed for the automatic generation of fuzzy decision trees is applied to both classification and regression problems. In regression problems, it is seen that the continuity constraint imposed by the function representation of the fuzzy tree leads to substantial improvements in the quality of the regression and limits the tendency to overfitting. In classification, fuzzification provides a means of uncovering the structure of the probability distribution for the classification errors in attribute space. This allows the identification of regions for which the error rate of the tree is significantly lower than the average error rate, sometimes even below the Bayes misclassification rate.