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
Maintaining knowledge about temporal intervals
Communications of the ACM
A Generalized Definition of Rough Approximations Based on Similarity
IEEE Transactions on Knowledge and Data Engineering
The model of fuzzy variable precision rough sets
IEEE Transactions on Fuzzy Systems
Rough neuro-fuzzy structures for classification with missing data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Building a Rule-Based Classifier—A Fuzzy-Rough Set Approach
IEEE Transactions on Knowledge and Data Engineering
A self-organizing fuzzy neural network based on a growing-and-pruning algorithm
IEEE Transactions on Fuzzy Systems
Improving Classifier Performance Using Data with Different Taxonomies
IEEE Transactions on Knowledge and Data Engineering
On the generalization of fuzzy rough sets
IEEE Transactions on Fuzzy Systems
Fuzzy-Rough Sets Assisted Attribute Selection
IEEE Transactions on Fuzzy Systems
Attributes Reduction Using Fuzzy Rough Sets
IEEE Transactions on Fuzzy Systems
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This paper proposes a new approach that integrates neural networks with the fuzzy rough set to build a Rough Fuzzy Neural Network Classifier (RFNNC) in order to mine temporal patterns in clinical databases. The lower approximation hypothesis and fuzzy decision table with the fuzzy features are used to acquire the fuzzy decision classes for deciding on the attributes. By contemplating a subset of attributes, comprising of the temporal intervals, the lower approximations are devised in this work. Moreover the basic sets are attained from lower approximations are sorted into the decision classes. The discernibility of the decision classes is designed to delineate the temporal consistency degree between the objects of the sets, from which the reducts are acquired. Next, the attribute subset from the reducts is used for training the fuzzy neural network to infer fuzzy rules. The induced rules will result with temporal patterns for classification. The fuzzy neural network has completely used the competence of fuzzy rough set theory to condense huge quantity of superfluous data. The effectiveness of this method is compared with other classifiers such as fuzzy rule based classifier to evaluate the accuracy of the proposed fuzzy neural network classifier. Experiments have been performed on the diabetic dataset and the simulation results induced proves that the proposed fuzzy neural network classifier on medical diabetic dataset stays as a corroboration for predicting the severity of the disease and exactness in decision support system.