Iterative vs Simultaneous Fuzzy Rule Induction


Autoria(s): Galea, Michelle; Shen, Qiang
Contribuinte(s)

Department of Computer Science

Advanced Reasoning Group

Data(s)

23/01/2008

23/01/2008

2005

Resumo

M. Galea and Q. Shen. Iterative vs Simultaneous Fuzzy Rule Induction. Proceedings of the 14th International Conference on Fuzzy Systems, pages 767-772.

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.

Non peer reviewed

Formato

6

Identificador

Galea , M & Shen , Q 2005 , ' Iterative vs Simultaneous Fuzzy Rule Induction ' pp. 767-772 . DOI: 10.1109/FUZZY.2005.1452491

PURE: 74504

PURE UUID: 7d98ac1f-fe5d-4449-ac69-854f9516a7b5

dspace: 2160/469

http://hdl.handle.net/2160/469

http://dx.doi.org/10.1109/FUZZY.2005.1452491

Idioma(s)

eng

Tipo

/dk/atira/pure/researchoutput/researchoutputtypes/contributiontoconference/paper

Relação

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