Including a simplicity criterion in the selection of the best rule in a genetic fuzzy learning algorithm

Abstract

Learning algorithms can obtain very useful descriptions of several problems. Many different alternative descriptions can be generated. In many cases, a simple description is preferable since it has a higher possibility of being valid in unseen cases and also it is usually easier to understand by a human expert. Thus, the main idea of this paper is to propose simplicity criteria and to include them in a learning algorithm. In this case, the learning algorithm will reward the simplest descriptions. We study simplicity criteria in the selection of fuzzy rules in the genetic fuzzy learning algorithm called SLAVE.