Name
Title | ||
|---|---|---|
Learning concurrently data and rule bases of Mamdani fuzzy rule-based systems by exploiting a novel interpretability index |
Abstract | ||
|---|---|---|
Interpretability of Mamdani fuzzy rule-based systems (MFRBSs) has been widely discussed in the last years, especially in the framework of multi-objective evolutionary fuzzy systems (MOEFSs). Here, multi-objective evolutionary algorithms (MOEAs) are applied to generate a set of MFRBSs with different trade-offs between interpretability and accuracy. In MOEFSs interpretability has often been measured in terms of complexity of the rule base and only recently partition integrity has also been considered. In this paper, we introduce a novel index for evaluating the interpretability of MFRBSs, which takes both the rule base complexity and the data base integrity into account. We discuss the use of this index in MOEFSs, which generate MFRBSs by concurrently learning the rule base, the linguistic partition granularities and the membership function parameters during the evolutionary process. The proposed approach has been experimented on six real world regression problems and the results have been compared with those obtained by applying the same MOEA, with only accuracy and complexity of the rule base as objectives. We show that our approach achieves the best trade-offs between interpretability and accuracy. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/s00500-010-0629-4 | Soft Comput. |
Keywords | Field | DocType |
evolutionary process,multi-objective evolutionary fuzzy system,mamdani fuzzy rule-based system,data base integrity,rule base,novel interpretability index,moefss interpretability,different trade-offs,multi-objective evolutionary algorithm,accuracy-interpretability trade-off � granularity learninginterpretability index � multi-objective evolutionary fuzzy systems � piecewise linear transformation,best trade-offs,concurrently data,rule base complexity | Interpretability,Data mining,Evolutionary algorithm,Computer science,Piecewise linear transformation,Artificial intelligence,Regression problems,Fuzzy control system,Membership function,Machine learning,Fuzzy rule based systems,Fuzzy rule | Journal |
Volume | Issue | ISSN |
15 | 10 | 1433-7479 |
Citations | PageRank | References |
16 | 0.55 | 34 |
Authors | ||
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 |