Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
A neuro-fuzzy method to learn fuzzy classification rules from data
Fuzzy Sets and Systems - Special issue: application of neuro-fuzzy systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
The design of self-organizing polynomial neural networks
Information Sciences—Informatics and Computer Science: An International Journal
A GA-based fuzzy modeling approach for generating TSK models
Fuzzy Sets and Systems - Modeling and control
A modified PNN algorithm with optimal PD modeling using the orthogonal least squares method
Information Sciences—Informatics and Computer Science: An International Journal
A local boosting algorithm for solving classification problems
Computational Statistics & Data Analysis
Boosting and other ensemble methods
Neural Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Fuzzy classifications using fuzzy inference networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Selection of relevant features in a fuzzy genetic learningalgorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An empirical risk functional to improve learning in a neuro-fuzzy classifier
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
GA-fuzzy modeling and classification: complexity and performance
IEEE Transactions on Fuzzy Systems
Self-adaptive neuro-fuzzy inference systems for classification applications
IEEE Transactions on Fuzzy Systems
Induction of fuzzy-rule-based classifiers with evolutionary boosting algorithms
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
Multimedia Tools and Applications
Construction of a neuron-fuzzy classification model based on feature-extraction approach
Expert Systems with Applications: An International Journal
Design of fuzzy radial basis function-based polynomial neural networks
Fuzzy Sets and Systems
A diversity-driven structure learning algorithm for building hierarchical neuro-fuzzy classifiers
Information Sciences: an International Journal
Information Sciences: an International Journal
Fuzzy classifier based on fuzzy support vector machine
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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A novel self-organizing neuro-fuzzy multilayered classifier (SONeFMUC) is suggested in this paper which is composed of small-scale interconnected fuzzy neuron classifiers (FNCs) arranged in layers. The model provides a different perspective for generating a new class of hierarchical classifiers with multilevel classifiers combination. At each layer, parent FNCs are combined to construct a descendant FNC at the next layer with enhanced classification qualities. The generic FNCs exhibit an original structure including four modules: (1) The fuser aggregates the decision support outputs of the parent FNCs using a fusion operator. (2) The data splitting module divides data into correctly classified patterns with high degree of confidence which are handled by the fuser and ambiguous ones. (3) The last two modules are realized by fuzzy rule-based systems and implement a neuro-fuzzy classifier within each FNC, used to improve the classification accuracy of ambiguous patterns. The former performs feature transformations to an intermediate output space while the latter provides the decision supports of non-confident patterns. Unlike traditional classifiers, the classification mapping is accomplished in SONeFMUC by performing successive decision fusions and feature transformations. SONeFMUC structure is determined sequentially via a self-constructing learning algorithm. Structure learning inherently implements feature selection, considering the most informative attributes as model inputs. To improve classification accuracy, the models obtained after structure learning are optimized using a parameter learning scheme based on genetic algorithms. The effectiveness of our approach is tested on a set of benchmark classification problems. Experimental results show that the proposed neuro-fuzzy classifier is favorably compared to other well-known classification techniques of the literature.