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Extracting Decision Rules from Qualitative Data Using Sugeno Integral: A Case-Study

Part of the Lecture Notes in Computer Science book series (LNAI,volume 9161)


This paper deals with knowledge extraction from experimental data in multifactorial evaluation using Sugeno integrals. They are qualitative criteria aggregations where it is possible to assign weights to groups of criteria. A method for deriving such weights from data is recalled. We also present results in the logical representation of Sugeno integrals. Then we show how to extract if-then rules expressing the selection of good situations on the basis of local evaluations, and rules to detect bad situations. We illustrate such methods on a case-study in the area of water ecosystem health.


  • Alkaline Phosphatase Activity
  • Global Evaluation
  • Possibility Distribution
  • Possibilistic Logic
  • Elimination Rule

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  1. 1.

    In order to compare \(\max _{k} \check{\mu }_{x^k,\alpha _k}\) and \(\min _k \hat{\mu }_{x^k,\alpha _k}\) it is not necessary to calculate their values and to compare them on each subset of criteria. It is proved in [14] that the set of compatible capacities is not empty if and only if for all \(\alpha _ k < \alpha _l\) we have \(\{ i| x^l_i \ge \alpha _l\} \not \subseteq \{ i| x^k_i > \alpha _k\}\).


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Dubois, D., Durrieu, C., Prade, H., Rico, A., Ferro, Y. (2015). Extracting Decision Rules from Qualitative Data Using Sugeno Integral: A Case-Study. In: Destercke, S., Denoeux, T. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2015. Lecture Notes in Computer Science(), vol 9161. Springer, Cham.

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