A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handwritten numerical recognition based on multiple algorithms
Pattern Recognition
Original Contribution: Stacked generalization
Neural Networks
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining the results of several neural network classifiers
Neural Networks
Democracy in neural nets: voting schemes for classification
Neural Networks
Methods for combining experts' probability assessments
Neural Computation
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Fusion of handwritten word classifiers
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
A theory of classifier combination: the neural network approach
A theory of classifier combination: the neural network approach
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Optimal linear combinations of neural networks
Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Experimental evaluation of expert fusion strategies
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Pattern Recognition Letters
A Theoretical Study on Six Classifier Fusion Strategies
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Remote Sensing Digital Image Analysis: An Introduction
Remote Sensing Digital Image Analysis: An Introduction
Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing
Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
A theory of classifier combination: the neural network approach
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
A Theoretical and Experimental Analysis of Linear Combiners for Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
A cluster validity index for fuzzy clustering
Pattern Recognition Letters
Cost-conscious classifier ensembles
Pattern Recognition Letters
Neuro-Fuzzy Fusion: A New Approach to Multiple Classifier System
ICIT '06 Proceedings of the 9th International Conference on Information Technology
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Trainable fusion rules. I. Large sample size case
Neural Networks
Trainable fusion rules. II. Small sample-size effects
Neural Networks
A novel fuzzy classifier based on product aggregation operator
Pattern Recognition
Boosting and other ensemble methods
Neural Computation
Issues in stacked generalization
Journal of Artificial Intelligence Research
Remote sensing image classification: a neuro-fuzzy MCS approach
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
Switching between selection and fusion in combining classifiers: anexperiment
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
Mixture of experts for classification of gender, ethnic origin, and pose of human faces
IEEE Transactions on Neural Networks
Multiple network fusion using fuzzy logic
IEEE Transactions on Neural Networks
Improving model accuracy using optimal linear combinations of trained neural networks
IEEE Transactions on Neural Networks
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A neuro-fuzzy-combiner (NFC) is proposed to design an efficient multiple classifier system (MCS) with an aim to have an effective solution scheme for difficult classification problems. Although, a number of combiners exist in the literature, they do not provide consistently good performance on different datasets. In this scenario: 1) we propose an effective multiple classifier system (MCS) based on NFC that fuses the output of a set of fuzzy classifiers; 2) conduct an extensive experimental analysis to justify the effectiveness of the proposed NFC. In the proposed technique, we used a neural network to combine the output of a set of fuzzy classifiers using the principles of neuro-fuzzy hybridisation. The neural combiner adaptively learns its parameters depending on the input data, and thus the output is robust. Superiority of the proposed combiner has been demonstrated experimentally on five standard datasets and two remote sensing images. It performed consistently better than the existing combiners over all the considered datasets.