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Fuzzy inference system as an aggregation operator: application to the design of a soil chemical quality index

Abstract : Fuzzy logic is widely used in linguistic modeling. In this work, fuzzy logic is used in a multicriteria decision making framework in two different ways. First, fuzzy sets are used to model an expert preference relation for each of the individual information sources to turn raw data into satisfaction degrees. Second, fuzzy rules are used to model the interaction between sources to aggregate the individual degrees into a global score. The whole framework is implemented as an open source software called GeoFIS. The potential of the method is illustrated using an agronomic case study to design a soil chemical quality index from expert knowledge for cacao production systems. The data come from three municipalities of Tolima department in Colombia. The output inferred by the fuzzy inference system was used as a target to learn the weights of classical numerical aggregation operators. Only the Choquet Integral proved to have a similar modeling ability, but the weights would have been difficult to set from expert knowledge without learning.
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https://hal.inrae.fr/hal-02972210
Contributor : Isabelle Nault <>
Submitted on : Tuesday, October 20, 2020 - 11:28:25 AM
Last modification on : Friday, March 19, 2021 - 3:32:39 AM

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Denys Yohana Mora-Herrera, Serge Guillaume, Didier Snoeck, Orlando Zúñiga Escobar. Fuzzy inference system as an aggregation operator: application to the design of a soil chemical quality index. 18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Jun 2020, Lisbon, Portugal. pp.447-456, ⟨10.1007/978-3-030-50143-3_35⟩. ⟨hal-02972210⟩

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