Abstract
Aim of the paper is the application of a Learning Classifier System (LCS) to learn the inference rules in a Fuzzy Decision Support System (FDSS). The FDSS is used for the Environmental Risk Assessment (ERA) of the deliberate release of genetically modified plants. The evaluation process permits identifying potential impacts that can achieve one or more receptors through a set of migration paths. The risk assessment in the FDSS is obtained by using a Fuzzy Inference System performed using jFuzzyLogic library. For the human experts might be hard developing complex FISs. We propose to use a LCS for automatically learning the appropriate fuzzy rules from the questionnaires produced by notifiers, named Fuzzy Rule Learning System (FRLS). FRLS is based on a special kind of LCS, namely the eXtended Classifier System (XCS). The derived rules have been validated on real world cases by the human experts that are in charge of ERA.
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Camastra, F. et al. (2013). Rule Learning in a Fuzzy Decision Support System for the Environmental Risk Assessment of GMOs. In: Masulli, F., Pasi, G., Yager, R. (eds) Fuzzy Logic and Applications. WILF 2013. Lecture Notes in Computer Science(), vol 8256. Springer, Cham. https://doi.org/10.1007/978-3-319-03200-9_23
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DOI: https://doi.org/10.1007/978-3-319-03200-9_23
Publisher Name: Springer, Cham
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