Name
Abstract | ||
|---|---|---|
In the last years, several papers have proposed to adopt multi-objective evolutionary algorithms (MOEAs) to generate Mamdani fuzzy rule-based systems with different trade-offs between interpretability and accuracy. Since interpretability is difficult to quantify because of its qualitative nature, several measures have been introduced, but there is no general agreement on any of them. In this paper, we propose an MOEA to learn concurrently rule base and membership function parameters by optimizing accuracy and interpretability, which is measured in terms of number of conditions in the antecedents of rules and partition integrity. Partition integrity is evaluated by using a purposely-defined index based on the piecewise linear transformation exploited to learn membership function parameters. Results on a real-world regression problem are shown and discussed. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-02319-4_74 | HAIS |
Keywords | Field | DocType |
last year,partition integrity,three-objective evolutionary approach,general agreement,concurrently rule base,mamdani fuzzy rule-based system,membership function parameter,piecewise linear transformation,multi-objective evolutionary algorithm,different trade-offs,generate mamdani fuzzy rule-based,optimizing accuracy,membership function,piecewise linear,indexation,rule based | Interpretability,Data mining,Evolutionary algorithm,Computer science,Piecewise linear transformation,Artificial intelligence,Regression problems,Partition (number theory),Membership function,Machine learning,Fuzzy rule,Fuzzy rule based systems | Conference |
Volume | ISSN | Citations |
5572 | 0302-9743 | 1 |
PageRank | References | Authors |
0.34 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michela Antonelli | 1 | 273 | 15.38 |
| Pietro Ducange | 2 | 566 | 27.63 |
| Beatrice Lazzerini | 3 | 715 | 45.56 |
| Francesco Marcelloni | 4 | 1404 | 91.43 |