2017

Conférence

In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 123-124)

During the last decades, Learning Classifier Systems have known many advancements that were highlighting their potential to resolve complex problems. Despite the advantages offered by these algorithms, it is important to tackle other aspects such as the uncertainty to improve their performance. In this paper, we present a new Learning Classifier System (LCS) that deals with uncertainty in the class selection in particular imprecision. Our idea is to integrate the Belief function theory in the sUpervised Classifier System (UCS) for classification purpose. The new approach proved to be efficient to resolve several classification problems.

@inproceedings{abdelkarim2017evidential,
  title={Evidential learning classifier system},
  author={Abdelkarim, Chedi and Rejeb, Lilia and Said, Lamjed Ben and Elarbi, Maha},
  booktitle={Proceedings of the Genetic and Evolutionary Computation Conference Companion},
  pages={123--124},
  year={2017}
}