Name
Abstract | ||
|---|---|---|
The most challenging problem in developing fuzzy rule-based classification systems is the construction of a fuzzy rule base for the target problem. In many practical applications, fuzzy sets that are of particular linguistic meanings, are often predefined by domain experts and required to be maintained in order to ensure interpretability of any subsequent inference results. However, learning fuzzy rules using fixed fuzzy quantity space without any qualification will restrict the accuracy of the resulting rules. Fortunately, adjusting the weights of fuzzy rules can help improve classification accuracy without degrading the interpretability. There have been different proposals for fuzzy rule weight tuning through the use of various heuristics with limited success. This paper proposes an alternative approach using Particle Swarm Optimisation in the search of a set of optimal rule weights, entailing high classification accuracy. Systematic experimental studies are carried out using common benchmark data sets, in comparison to popular rule based learning classifiers. The results demonstrate that the proposed approach can boost classification performance, especially when the size of the initially built rule base is relatively small, and is competitive to popular rule-based learning classifiers. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s00500-015-1922-z | Soft Computing - A Fusion of Foundations, Methodologies and Applications |
Keywords | Field | DocType |
Fuzzy rule induction, Fuzzy rule weights, Rule weight modification, Particle swarm optimisation | Data mining,Neuro-fuzzy,Fuzzy classification,Defuzzification,Fuzzy set operations,Computer science,Artificial intelligence,Adaptive neuro fuzzy inference system,Type-2 fuzzy sets and systems,Fuzzy number,Machine learning,Fuzzy rule | Journal |
Volume | Issue | ISSN |
20 | 8 | 1432-7643 |
Citations | PageRank | References |
5 | 0.41 | 26 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tianhua Chen | 1 | 42 | 7.16 |
| Qiang Shen | 2 | 864 | 55.09 |
| Pan Su | 3 | 82 | 11.72 |
| Changjing Shang | 4 | 212 | 34.92 |