Hierarchical fuzzy rule based classification systems with genetic rule selection for imbalanced data-sets
Metadatos
Mostrar el registro completo del ítemEditorial
Elsevier
Materia
Classification Fuzzy rule based classification systems Imbalanced data-sets Genetic fuzzy systems Genetic rule selection Hierarchical fuzzy partitions Inteligencia artificial Artificial intelligence
Fecha
2008-12-06Referencia bibliográfica
Alberto Fernández, María José del Jesus, Francisco Herrera, Hierarchical fuzzy rule based classification systems with genetic rule selection for imbalanced data-sets, International Journal of Approximate Reasoning, Volume 50, Issue 3, 2009, Pages 561-577, ISSN 0888-613X, [https://doi.org/10.1016/j.ijar.2008.11.004]
Patrocinador
Spanish Ministry of Education and Science (MEC) under Projects TIN-2005-08386-C05-01 and TIN-2005-08386- C05-03Resumen
In many real application areas, the data used are highly skewed and the number of
instances for some classes are much higher than that of the other classes. Solving a classification
task using such an imbalanced data-set is difficult due to the bias of the training
towards the majority classes.
The aim of this paper is to improve the performance of fuzzy rule based classification systems
on imbalanced domains, increasing the granularity of the fuzzy partitions on the
boundary areas between the classes, in order to obtain a better separability. We propose
the use of a hierarchical fuzzy rule based classification system, which is based on the
refinement of a simple linguistic fuzzy model by means of the extension of the structure
of the knowledge base in a hierarchical way and the use of a genetic rule selection process
in order to get a compact and accurate model.
The good performance of this approach is shown through an extensive experimental
study carried out over a large collection of imbalanced data-sets.