Fuzzy mathematical approach to pattern recognition
Fuzzy mathematical approach to pattern recognition
C4.5: programs for machine learning
C4.5: programs for machine learning
Fuzzy logic, neural networks, and soft computing
Communications of the ACM
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Machine Learning
Selection of optimal set of weights in a layered network using genetic algorithms
Information Sciences—Intelligent Systems: An International Journal
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Rough Sets: Theoretical Aspects of Reasoning about Data
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Genetic Algorithms in Search, Optimization and Machine Learning
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Symbolic Interpretation of Artificial Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Classifying Faults in High Voltage Power Systems: A Rough-Fuzzy Neural Computational Approach
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Neural Networks Design: Rough Set Approach to Continuous Data
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
IEEE Transactions on Neural Networks
Rough fuzzy MLP: knowledge encoding and classification
IEEE Transactions on Neural Networks
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
Data mining in soft computing framework: a survey
IEEE Transactions on Neural Networks
Line-crawling robot navigation: a rough neurocomputing approach
Autonomous robotic systems
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Pattern Recognition Letters - Special issue: Advances in pattern recognition
Rough-Fuzzy C-Medoids Algorithm and Selection of Bio-Basis for Amino Acid Sequence Analysis
IEEE Transactions on Knowledge and Data Engineering
RFCM: A Hybrid Clustering Algorithm Using Rough and Fuzzy Sets
Fundamenta Informaticae
Interpolating support information granules
Neurocomputing
Rough Sets in Hybrid Soft Computing Systems
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Maximum Class Separability for Rough-Fuzzy C-Means Based Brain MR Image Segmentation
Transactions on Rough Sets IX
Automatic fuzzy rules generation using fuzzy genetic algorithm
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 6
Research on rough set theory and applications in China
Transactions on rough sets VIII
So near and yet so far: New insight into properties of some well-known classifier paradigms
Information Sciences: an International Journal
Evolutionary computation and rough set-based hybrid approach to rule generation
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Rough sets in the Soft Computing environment
Information Sciences: an International Journal
RFCM: A Hybrid Clustering Algorithm Using Rough and Fuzzy Sets
Fundamenta Informaticae
Matroidal structure of rough sets and its characterization to attribute reduction
Knowledge-Based Systems
Rough matroids based on relations
Information Sciences: an International Journal
Title Natural computing: A problem solving paradigm with granular information processing
Applied Soft Computing
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A methodology is described for evolving a Rough-fuzzy multi layer perceptron with modular concept using a genetic algorithm to obtain a structured network suitable for both classification and rule extraction. The modular concept, based on "divide and conquer" strategy, provides accelerated training and a compact network suitable for generating a minimum number of rules with high certainty values. The concept of variable mutation operator is introduced for preserving the localized structure of the constituting knowledge-based subnetworks, while they are integrated and evolved. Rough set dependency rules are generated directly from the real valued attribute table containing fuzzy membership values. Two new indices viz., "certainty" and "confusion" in a decision are defined for evaluating quantitatively the quality of rules. The effectiveness of the model and the rule extraction algorithm is extensively demonstrated through experiments alongwith comparisons.