International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Linguistic recognition system based on approximate reasoning
Information Sciences: an International Journal
Fuzzy logic, neural networks, and soft computing
Communications of the ACM
Essentials of fuzzy modeling and control
Essentials of fuzzy modeling and control
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
Fuzzy engineering
A Comparative Analysis of Methods for Pruning Decision Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing
Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing
Hybrid Decision Tree Learners with Alternative Leaf Classifiers: An Empirical Study
Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference
Modular Neuro-Fuzzy Networks Used in Explicit and Implicit Knowledge Integration
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
Simple fuzzy logic rules based on fuzzy decision tree for classification and prediction problem
Intelligent information processing II
An adaptable Gaussian neuro-fuzzy classifier
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Fuzzy decision tree, linguistic rules and fuzzy knowledge-based network: generation and evaluation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Top-down induction of decision trees classifiers - a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Rule-base structure identification in an adaptive-network-based fuzzy inference system
IEEE Transactions on Fuzzy Systems
Conditional fuzzy clustering in the design of radial basis function neural networks
IEEE Transactions on Neural Networks
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
Subsethood-product fuzzy neural inference system (SuPFuNIS)
IEEE Transactions on Neural Networks
GenSoFNN: a generic self-organizing fuzzy neural network
IEEE Transactions on Neural Networks
A neuro-fuzzy scheme for simultaneous feature selection and fuzzy rule-based classification
IEEE Transactions on Neural Networks
Asymmetric subsethood-product fuzzy neural inference system (ASuPFuNIS)
IEEE Transactions on Neural Networks
Construction of a neuron-fuzzy classification model based on feature-extraction approach
Expert Systems with Applications: An International Journal
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Inspired by a modular way of reasoning, we present a subsethood-product fuzzy neural classifier with a novel, robust and dynamic architecture, involving a main module and a number of submodules. The well-known classification and regression trees (CART) algorithm is employed as a fast preprocess of structure identification, which divides the input space into high certainty and low certainty regions, each representing a primary fuzzy rule. These primary fuzzy rules use a minimum set of attributes and are mapped onto the main neuro-fuzzy module. However, the patterns belonging to a low certainty primary rule get further split into a subset of secondary rules that use an extended set of attributes. Each such rule subset is mapped onto an expert-submodule, which gets activated only when a pattern falls into the respective low certainty region. In other words, we create a rule form of ''if-then-if'' conditional statement, where the first ''IF'' concerns the main module and the primary rule, while the second ''IF'' concerns the respective submodule and the secondary rule set. This dynamic resource-allocating model is optimized through a supervised learning procedure. Experiments in benchmark classification tasks prove that this architecture not only does reduce complexity and computational cost, which is its primary goal, but also offers fast and accurate processing during real-time operation. Moreover, it holds certain properties that make it ideal for soft computing applications of high dimension, especially those that adopt user-profiles or require partial re-training.