Non-convex clustering using expectation maximization algorithm with rough set initialization
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
Rough-Fuzzy MLP: Modular Evolution, Rule Generation, and Evaluation
IEEE Transactions on Knowledge and Data Engineering
Soft Computing Pattern Recognition: Principles, Integrations, and Data Mining
Proceedings of the Joint JSAI 2001 Workshop on New Frontiers in Artificial Intelligence
Feature Subset Selection by Neuro-rough Hybridization
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Towards Rough Neural Computing Based on Rough Membership Functions: Theory and Application
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
A Hybrid Model for Rule Discovery in Data
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Design of Rough Neurons: Rough Set Foundation and Petri Net Model
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
Line-crawling robot navigation: a rough neurocomputing approach
Autonomous robotic systems
Case Generation Using Rough Sets with Fuzzy Representation
IEEE Transactions on Knowledge and Data Engineering
Applied Intelligence
Two new operators in rough set theory with applications to fuzzy sets
Information Sciences—Informatics and Computer Science: An International Journal
Rough-Fuzzy C-Medoids Algorithm and Selection of Bio-Basis for Amino Acid Sequence Analysis
IEEE Transactions on Knowledge and Data Engineering
Integrating rough set theory and fuzzy neural network to discover fuzzy rules
Intelligent Data Analysis
RFCM: A Hybrid Clustering Algorithm Using Rough and Fuzzy Sets
Fundamenta Informaticae
Protein sequence analysis using relational soft clustering algorithms
International Journal of Computer Mathematics - Bioinformatics
Rough Evolutionary Fuzzy System Based on Interactive T-Norms
IBERAMIA '08 Proceedings of the 11th Ibero-American conference on AI: Advances in Artificial Intelligence
Precision of Rough Set Clustering
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Maximum Class Separability for Rough-Fuzzy C-Means Based Brain MR Image Segmentation
Transactions on Rough Sets IX
Rough neuro voting system for data mining: application to stock price prediction
RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
Inference mechanism for polymer processing using rough-neuro fuzzy network
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Covering numbers in covering-based rough sets
RSFDGrC'11 Proceedings of the 13th international conference on Rough sets, fuzzy sets, data mining and granular computing
Fuzzy rough granular neural networks, fuzzy granules, and classification
Theoretical Computer Science
Incremental learning in AttributeNets with dynamic reduct and IQuickReduct
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Location-Aware data mining for mobile users based on neuro-fuzzy system
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Divisible rough sets based on self-organizing maps
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Class-dependent rough-fuzzy granular space, dispersion index and classification
Pattern Recognition
Dealing with missing data: algorithms based on fuzzy set and rough set theories
Transactions on Rough Sets IV
Rough sets in the Soft Computing environment
Information Sciences: an International Journal
RFCM: A Hybrid Clustering Algorithm Using Rough and Fuzzy Sets
Fundamenta Informaticae
Title Natural computing: A problem solving paradigm with granular information processing
Applied Soft Computing
International Journal of Approximate Reasoning
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A scheme of knowledge encoding in a fuzzy multilayer perceptron (MLP) using rough set-theoretic concepts is described. Crude domain knowledge is extracted from the data set in the form of rules. The syntax of these rules automatically determines the appropriate number of hidden nodes while the dependency factors are used in the initial weight encoding. The network is then refined during training. Results on classification of speech and synthetic data demonstrate the superiority of the system over the fuzzy and conventional versions of the MLP (involving no initial knowledge)