Evidential learning classifier system
Chedi Abdelkarim, Lilia Rejeb, Lamjed Ben Saïd, Maha Elarbi · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2017
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.