Loading...
Please wait, while we are loading the content...
Similar Documents
Iterative vs simultaneous fuzzy rule induction.
| Content Provider | CiteSeerX |
|---|---|
| Author | Galea, Michelle |
| Abstract | Abstract — Iterative rule learning is a common strategy for fuzzy rule induction using stochastic population-based algorithms (SPBAs) such as Ant Colony Optimisation (ACO) and genetic algorithms. Several SPBAs are run in succession with the result of each being a rule added to an emerging final ruleset. Each successive rule is generally produced without taking into account the rules already in the final ruleset, and how well they may interact during fuzzy inference. This popular approach is compared with the simultaneous rule learning strategy introduced here, whereby the fuzzy rules that form the final ruleset are evolved and evaluated together. This latter strategy is found to maintain or improve classification accuracy of the evolved ruleset, and simplify the ACO algorithm used here as the rule discovery mechanism by removing the need for one parameter, and adding robustness to value changes in another. This initial work also suggests that the rulesets may be obtained at less computational expense than when using an iterative rule learning strategy. I. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Latter Strategy Rule Discovery Mechanism Genetic Algorithm Ant Colony Optimisation Value Change Fuzzy Inference Iterative Rule Fuzzy Rule Induction Classification Accuracy Successive Rule Fuzzy Rule Computational Expense Evolved Ruleset Stochastic Population-based Algorithm Final Ruleset Aco Algorithm Simultaneous Rule Several Spbas Popular Approach Initial Work Abstract Iterative Rule Learning Common Strategy |
| Content Type | Text |
