Abstract
In this paper we propose a joint grid-based and stochastic search for parameters elicitation in the case of WOWA aggregation functions. The method uses a grid search approach to determine the parameter of a monotonic quantifier, and for each of the values, a stochastic search in the space of the criteria weights minimizes the sum of the quadratic error between the computed and the real output of a learning set. A simulated application is proposed in the case of transportation risk assessment, using an ad hoc questionnaire applied to risk matrices.
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Cardin, M., Giove, S. (2015). A Grid-Based Optimization Algorithm for Parameters Elicitation in WOWA Operators: An Application to Risk Assesment. In: Bassis, S., Esposito, A., Morabito, F. (eds) Advances in Neural Networks: Computational and Theoretical Issues. Smart Innovation, Systems and Technologies, vol 37. Springer, Cham. https://doi.org/10.1007/978-3-319-18164-6_20
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DOI: https://doi.org/10.1007/978-3-319-18164-6_20
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18163-9
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