RT Journal Article
T1 A bipolar knowledge representation model to improve supervised fuzzy classification algorithms.
A1 Villarino, Guillermo
A1 Gomez, Daniel
A1 Rodríguez, Juan Tinguaro
A1 Montero, Javier
AB Most supervised classification algorithms produce a soft score (either a probability, a fuzzy degree, a possibility, a cost, etc.) assessing the strength of the association between items and classes. After that, each item is assigned to the class with the highest soft score. In this paper, we show that this last step can be improved through alternative procedures more sensible to the available soft information. To this aim, we propose a general fuzzy bipolar approach that enables learning how to take advantage of the soft information provided by many classification algorithms in order to enhance the generalization power and accuracy of the classifiers. To show the suitability of the proposed approach, we also present some computational experiences for binary classification problems, in which its application to some well-known classifiers as random forest, classification trees and neural networks produces a statistically significant improvement in the performance of the classifiers.
PB Springer-Verlag
SN 1432-7643
YR 2018
FD 2018
LK https://hdl.handle.net/20.500.14352/18618
UL https://hdl.handle.net/20.500.14352/18618
LA eng
NO Ministerio de Economía y Competitividad (MINECO)
NO Ministerio de Educación y Ciencia (MEC)
DS Docta Complutense
RD 29 abr 2024