Overview of the SLAVE learning algorithm: A review of its evolution and prospects
Metadatos
Mostrar el registro completo del ítemEditorial
Atlantis
Materia
Classification problems Feature selection Fuzzy Rules Genetic algorithms Inteligencia artificial Artificial intelligence
Fecha
2014-12-01Referencia bibliográfica
García, D., González, A., & Pérez, R. (2014). Overview of the slave learning algorithm: A review of its evolution and prospects. International Journal of Computational Intelligence Systems, 7(6), 1194-1221. DOI: [https://doi.org/10.1080/18756891.2014.967008]
Patrocinador
Andalusian Regional Government project P09-TIC-04813; Spanish Government TIN2012-38969; European CommissionResumen
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.