Name
Abstract | ||
|---|---|---|
In many practical applications of classifiers, not only high accuracy but also high interpretability is required. Among a wide variety of existing classifiers, Takagi-Sugeno-Kang (TSK) fuzzy classifiers may be one of the best choices for achieving a good balance between interpretability and accuracy. In order to further improve their accuracy without losing their interpretability, we propose a hig... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TFUZZ.2017.2729507 | IEEE Transactions on Fuzzy Systems |
Keywords | Field | DocType |
Pragmatics,Fuzzy sets,Takagi-Sugeno model,Machine learning,Optimization,Complexity theory,Pattern classification | Fuzzy classification,Fuzzy set operations,Artificial intelligence,Fuzzy associative matrix,Fuzzy number,Neuro-fuzzy,Pattern recognition,Defuzzification,Fuzzy logic,Linguistics,Mathematics,Machine learning,Fuzzy rule | Journal |
Volume | Issue | ISSN |
26 | 3 | 1063-6706 |
Citations | PageRank | References |
24 | 0.75 | 21 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuanpeng Zhang | 1 | 30 | 2.54 |
| Hisao Ishibuchi | 2 | 7385 | 503.41 |
| Shitong Wang | 3 | 1485 | 109.13 |