Skip to Main content Skip to Navigation

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.
Document type :
Complete list of metadata
Contributor : Rallou Thomopoulos Connect in order to contact the contributor
Submitted on : Tuesday, June 18, 2013 - 11:33:20 AM
Last modification on : Friday, August 5, 2022 - 3:03:00 PM


  • HAL Id : lirmm-00835217, version 1
  • PRODINRA : 315713


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



Record views