Overview of the SLAVE learning algorithm: A review of its evolution and prospects

Abstract

Inductive learning has been—and still is—one of the most important methods that can be applied in classification problems. Knowledge is usually represented using rules that establish relationships between the problem variables. SLAVE (Structural Learning Algorithm in a Vague Environment) was one of the first fuzzy-rule learning algorithms, and since its first implementation in 1994 it has been frequently used to benchmark new algorithms. Over time, the algorithm has undergone several modifications, and identifying the different versions developed is not an easy task. In this work we present a study of the evolution of the SLAVE algorithm from 1996 to date, marking the most important landmarks as definitive versions. In order to add these final versions to the KEEL platform, Java implementations have been developed. Finally, we describe the parameters used and the results obtained in the experimental study.