2017

Conférence

Authors: Chedi Abdelkarim, Lilia Rejeb, Lamjed Ben Said, Maha ElarbiAuthors Info & Claims GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion Pages 123 - 124 https://doi.org/10.1145/3067695.3075997

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{10.1145/3067695.3075997,
author = {Abdelkarim, Chedi and Rejeb, Lilia and Said, Lamjed Ben and Elarbi, Maha},
title = {Evidential learning classifier system},
year = {2017},
isbn = {9781450349390},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3067695.3075997},
doi = {10.1145/3067695.3075997},
abstract = {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.},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion},
pages = {123–124},
numpages = {2},
keywords = {uncertainty, machine learning, learning classifier systems, classification, belief function theory},
location = {Berlin, Germany},
series = {GECCO '17}
}