Hyperrectangles Selection for Monotonic Classification by Using Evolutionary Algorithms
Metadatos
Afficher la notice complèteEditorial
ATLANTIS PRESS
Materia
Monotonic Classification Nested Generalized Examples Evolutionary algorithms Rule Induction Instance-based Learning
Date
2016Referencia bibliográfica
Garcia, J., AlBar, A. M., Aljohani, N. R., Cano, J., & Garcia, S. (2016). Hyperrectangles selection for monotonic classification by using evolutionary algorithms. International Journal of Computational Intelligence Systems, 9(1), 184-201. [doi: 10.1080/18756891.2016.1146536]
Patrocinador
TIN2014-57251-PRésumé
In supervised learning, some real problems require the response attribute to represent ordinal values that
should increase with some of the explaining attributes. They are called classification problems with monotonicity constraints. Hyperrectangles can be viewed as storing objects in Rn which can be used to learn
concepts combining instance-based classification with the axis-parallel rectangle mainly used in rule induction systems. This hybrid paradigm is known as nested generalized exemplar learning. In this paper,
we propose the selection of the most effective hyperrectangles by means of evolutionary algorithms to
tackle monotonic classification. The model proposed is compared through an exhaustive experimental
analysis involving a large number of data sets coming from real classification and regression problems.
The results reported show that our evolutionary proposal outperforms other instance-based and rule learning models, such as OLM, OSDL, k-NN and MID; in accuracy and mean absolute error, requiring a fewer
number of hyperrectangles.