A Hybrid, Case-Based Related Approach to Generate Predictions from Rules

Fatiha Saïs 1 Rallou Thomopoulos 2, 3
3 GRAPHIK - Graphs for Inferences on Knowledge
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : This work takes place in the general context of the construction of a prediction for decision support issues. It relies on knowledge expressed by numerous rules with homogeneous structure, extracted from various scientific publications of a domain. In this paper we propose a predictive approach that allows one to perform two stages: firstly, the generation of a partition of the rules into groups that express a common experimental tendency; secondly, the computation of a prediction rule, starting from a new description of experimental conditions and from the obtained groups of rules.The method is experimented on a case study in food science. Compared to the results that are obtained by a classical approach based on a decision tree classifier, the proposed method obtains good predictions, in the sense of accuracy, completeness and error rate.
Type de document :
Rapport
[Research Report] RR-13022, LIRMM. 2013
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00835217
Contributeur : Rallou Thomopoulos <>
Soumis le : mardi 18 juin 2013 - 11:33:20
Dernière modification le : jeudi 24 mai 2018 - 15:59:22

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  • HAL Id : lirmm-00835217, version 1

Citation

Fatiha Saïs, Rallou Thomopoulos. A Hybrid, Case-Based Related Approach to Generate Predictions from Rules. [Research Report] RR-13022, LIRMM. 2013. 〈lirmm-00835217〉

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