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Reports (Research Report) Year : 2013

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

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.
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Dates and versions

lirmm-00835217 , version 1 (18-06-2013)

Identifiers

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

Cite

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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