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Erschienen in:
2011 | OriginalPaper | Buchkapitel
A Generalized Fuzzy-Rough Set Application for Forest Fire Risk Estimation Feature Reduction
verfasst von : T. Tsataltzinos, L. Iliadis, S. Spartalis
Erschienen in: Artificial Intelligence Applications and Innovations
Verlag: Springer Berlin Heidelberg
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This paper aims in the reduction of data attributes of a fuzzy-set based system for the estimation of forest fire risk in Greece, with the use of rough-set theory. The aim is to get as good results as possible with the use of the minimum amount of data attributes possible. Data manipulation for this project is done in MS-Access. The resulting data table is inserted into Matlab in order to be fuzzified. The final result of this clustering is inserted into Rossetta, which is a Rough set exploration software, in order to estimate the reducts. The risk estimation is recalculated with the use of the reduct set in order to measure the accuracy of the final minimum attribute set. Nine forest fire risk factors were taken into consideration for the purpose of this paper and the Greek terrain was separated into smaller areas, each concerning a different Greek forest department.