Axiomatics for fuzzy rough sets
Fuzzy Sets and Systems
A comparative study of fuzzy rough sets
Fuzzy Sets and Systems
Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches
IEEE Transactions on Knowledge and Data Engineering
Expert Systems with Applications: An International Journal
On the topological properties of fuzzy rough sets
Fuzzy Sets and Systems
Evolutionary computing for knowledge discovery in medical diagnosis
Artificial Intelligence in Medicine
Fuzzy Rough Sets: The Forgotten Step
IEEE Transactions on Fuzzy Systems
Fuzzy-rough nearest neighbour classification
Transactions on rough sets XIII
Fuzzy-rough nearest neighbour classification and prediction
Theoretical Computer Science
Hi-index | 0.00 |
This study presents a rough-fuzzy hybridization method to generate fuzzy if-then rules automatically from a medical diagnosis dataset with quantitative data values, based on fuzzy set and rough set theory. The proposed method consists of four stages: preprocessing inputs with fuzzy linguistic representation; rough set theory in finding notable reducts; candidate fuzzy if-then rules generation by data summarization, and truth evaluation the effectiveness of fuzzy if-then rules. The main contributions of the proposed method are the capability of fuzzy linguistic representation of the fuzzy if-then rules, finding concise fuzzy if-then rules from medical diagnosis dataset, and tolerance of imprecise data.