Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
Introduction to artificial neural systems
Introduction to artificial neural systems
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing
Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing
A Fuzzy Min-Max Neural Network Classifier with Compensatory Neuron Architecture
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Genetic granular classifiers in modeling software quality
Journal of Systems and Software
A Reflex Fuzzy Min Max Neural Network for Granular Data Classification
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
An inclusion/exclusion fuzzy hyperbox classifier
International Journal of Knowledge-based and Intelligent Engineering Systems - Advanced Intelligent Techniques in Engineering Applications
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Object recognition using reflex fuzzy min-max neural network with floating neurons
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
A survey of fuzzy clustering algorithms for pattern recognition. I
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Granular clustering: a granular signature of data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Recursive information granulation: aggregation and interpretation issues
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy logic = computing with words
IEEE Transactions on Fuzzy Systems
General fuzzy min-max neural network for clustering and classification
IEEE Transactions on Neural Networks
A Fuzzy Min-Max Neural Network Classifier With Compensatory Neuron Architecture
IEEE Transactions on Neural Networks
M-FMCN: modified fuzzy min-max classifier using compensatory neurons
AIKED'12 Proceedings of the 11th WSEAS international conference on Artificial Intelligence, Knowledge Engineering and Data Bases
Evolving granular neural networks from fuzzy data streams
Neural Networks
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Granular data classification and clustering is an upcoming and important issue in the field of pattern recognition. Conventionally, computing is thought to be manipulation of numbers or symbols. However, human recognition capabilities are based on ability to process nonnumeric clumps of information (information granules) in addition to individual numeric values. This paper proposes a granular neural network (GNN) called granular reflex fuzzy min-max neural network (GrRFMN) which can learn and classify granular data. GrRFMN uses hyperbox fuzzy set to represent granular data. Its architecture consists of a reflex mechanism inspired from human brain to handle class overlaps. The network can be trained online using granular or point data. The neuron activation functions in GrRFMN are designed to tackle data of different granularity (size). This paper also addresses an issue to granulate the training data and learn from it. It is observed that such a preprocessing of data can improve performance of a classifier. Experimental results on real data sets show that the proposed GrRFMN can classify granules of different granularity more correctly. Results are compared with general fuzzy min-max neural network (GFMN) proposed by Gabrys and Bargiela and with some classical methods.