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Communication Dans Un Congrès Année : 2020

Improving on coalitional prediction explanation

Résumé

Machine learning has proven increasingly essential in many fields but a lot obstacles still hinder its use by non-experts. The lack of trust in the results obtained is foremost among them, and has inspired several explanatory approaches in the literature. These approaches provide a great insight on the predictions of a model, but at a cost of a long computation time. In this paper, we aim to further improve the detection of relevant attributes influencing a prediction, on the strength of feature selection methods.
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Dates et versions

hal-03138314 , version 1 (11-02-2021)

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Gabriel Ferrettini, Julien Aligon, Chantal Soulé-Dupuy. Improving on coalitional prediction explanation. 24th European Conference on Advances in Databases and Information Systems (ADBIS 2020), Aug 2020, Lyon, France. pp.122-135, ⟨10.1007/978-3-030-54832-2_11⟩. ⟨hal-03138314⟩
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