A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
A neuro-fuzzy method to learn fuzzy classification rules from data
Fuzzy Sets and Systems - Special issue: application of neuro-fuzzy systems
The design of self-organizing polynomial neural networks
Information Sciences—Informatics and Computer Science: An International Journal
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A modified PNN algorithm with optimal PD modeling using the orthogonal least squares method
Information Sciences—Informatics and Computer Science: An International Journal
Is there a need for fuzzy logic?
Information Sciences: an International Journal
Induction of multiple fuzzy decision trees based on rough set technique
Information Sciences: an International Journal
Greedy regression ensemble selection: Theory and an application to water quality prediction
Information Sciences: an International Journal
Feature-based classifier ensembles for diagnosing multiple faults in rotating machinery
Applied Soft Computing
A multilayered neuro-fuzzy classifier with self-organizing properties
Fuzzy Sets and Systems
International Journal of Remote Sensing
Diversity of ability and cognitive style for group decision processes
Information Sciences: an International Journal
Incremental construction of classifier and discriminant ensembles
Information Sciences: an International Journal
An efficient structure learning algorithm for a self-organizing neuro-fuzzy multilayered classifier
MED '09 Proceedings of the 2009 17th Mediterranean Conference on Control and Automation
Improving generalization of fuzzy IF-THEN rules by maximizing fuzzy entropy
IEEE Transactions on Fuzzy Systems
Ectropy of diversity measures for populations in Euclidean space
Information Sciences: an International Journal
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
An efficient fuzzy classifier with feature selection based on fuzzyentropy
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
GA-fuzzy modeling and classification: complexity and performance
IEEE Transactions on Fuzzy Systems
Fuzzy polynomial neural networks: hybrid architectures of fuzzy modeling
IEEE Transactions on Fuzzy Systems
Self-adaptive neuro-fuzzy inference systems for classification applications
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
Cascaded classification of high resolution remote sensing images using multiple contexts
Information Sciences: an International Journal
Information Sciences: an International Journal
Information Sciences: an International Journal
Information Sciences: an International Journal
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In this article, an efficient structure learning algorithm is proposed for the development of self-organizing neuro-fuzzy multilayered classifiers (SONeFMUC). These classifiers are hierarchical structures comprising small-scale fuzzy-neuron classifiers (FNCs), interconnected along multiple layers. At each layer, parent FNCs are combined to construct a descendant FNC at the next layer with enhanced classification qualities. The SONeFMUC structure is progressively expanded by generating new layers based on the principles of the Group Method of Data Handling (GMDH) algorithm, which is appropriately adapted to handle classification tasks. Traditional GMDH proceeds blindly to the construction of all possible parent FNC pairs from the previous layer to obtain the individuals in the next layer without paying due attention to the diversity of the FNC combinations. However, previous experimentation shows that a large number of descendant FNCs exhibit similar or slightly better classification capabilities than their parent FNCs. This causes an increase of the computational cost required for structure learning, without a direct impact on the accuracy of the obtained models. In this paper, a modified version of GMDH is devised for effective identification of the SONeFMUC structure. We incorporate the Proportion of Specific Agreement (Ps) as a means to evaluate the diversity of the FNC pairs. In the devised method, only complementary FNCs are combined, i.e., FNCs which commit errors at different pattern subspaces, to construct a descendant FNC at the next layer. Accordingly, a computational reduction is achieved while high classification accuracy is maintained. The efficiency of the proposed structure learning is tested on a diverse set of benchmark datasets using land cover classification from multispectral images as a real-world application.