Case-based classifiers with fuzzy rough sets
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Fuzzy rough set approach based classifier
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
Classification based on association rules: A lattice-based approach
Expert Systems with Applications: An International Journal
A self learning rough fuzzy neural network classifier for mining temporal patterns
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
CAR-Miner: An efficient algorithm for mining class-association rules
Expert Systems with Applications: An International Journal
Knowledge reduction for decision tables with attribute value taxonomies
Knowledge-Based Systems
Set-based granular computing: A lattice model
International Journal of Approximate Reasoning
An improved algorithm for calculating fuzzy attribute reducts
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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The fuzzy-rough set (FRS) methodology, as a useful tool to handle discernibility and fuzziness, has been widely studied. Some researchers studied on the rough approximation of fuzzy sets, while some others focused on studying one application of FRS: attribute reduction (i.e., feature selection). However, constructing classifier by using FRS, as another application of FRS, has been less studied. In this paper, we build a rule-based classifier by using one generalized FRS model after proposing a new concept named as “consistence degree” which is used as the critical value to keep the discernibility information invariant in the processing of rule induction. First, we generalized the existing FRS to a robust model with respect to misclassification and perturbation by incorporating one controlled threshold into knowledge representation of FRS. Second, we propose a concept named as “consistence degree” and by the strict mathematical reasoning, we show that this concept is reasonable as a critical value to reduce redundant attribute values in database. By employing this concept, we then design a discernibility vector to develop the algorithms of rule induction. The induced rule set can function as a classifier. Finally, the experimental results show that the proposed rule-based classifier is feasible and effective on noisy data.