Identification of explanatory variables for DMU preparation process evaluation by using machine learning techniques - Archive ouverte HAL
Communication Dans Un Congrès Année : 2016

Identification of explanatory variables for DMU preparation process evaluation by using machine learning techniques

Résumé

Being able to estimate a priori the impact of DMU preparation scenarios for a dedicated activity would help identifying the best scenario from the beginning. Machine learning techniques are a means to a priori evaluate a DMU preparation process without to perform it by predicting its criteria of evaluation. For that, a representative database of examples must be developed that contains the right explanative and output variables. However, the key explanative variables are not clearly identified. This paper proposes a method for the selection of the most significant explanatory variables among all the database variables. In addition to using these variables for learning, this will allow to formalize the knowledge.
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Dates et versions

hal-02301469 , version 1 (30-09-2019)

Identifiants

  • HAL Id : hal-02301469 , version 1

Citer

Florence Danglade, Jean-Philippe Pernot, Philippe Veron, Lionel Fine. Identification of explanatory variables for DMU preparation process evaluation by using machine learning techniques. Virtual Concept International Workshop, 2016, Bordeaux, France. pp.1-4. ⟨hal-02301469⟩
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