@misc{10481/84822,
year = {2023},
month = {7},
url = {https://hdl.handle.net/10481/84822},
abstract = {Imprecise classification is a relatively new task within Machine Learning. The difference
with standard classification is that not only is one state of the variable under study determined, a set
of states that do not have enough information against them and cannot be ruled out is determined
as well. For imprecise classification, a mode called an Imprecise Credal Decision Tree (ICDT) that
uses imprecise probabilities and maximum of entropy as the information measure has been presented.
A difficult and interesting task is to show how to combine this type of imprecise classifiers.
A procedure based on the minimum level of dominance has been presented; though it represents a
very strong method of combining, it has the drawback of an important risk of possible erroneous
prediction. In this research, we use the second-best theory to argue that the aforementioned type of
combination can be improved through a new procedure built by relaxing the constraints. The new
procedure is compared with the original one in an experimental study on a large set of datasets, and
shows improvement.},
organization = {UGR-FEDER funds under Project A-TIC-344-UGR20},
organization = {FEDER/Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento
y Universidades” under Project P20_00159},
publisher = {MDPI},
keywords = {Imprecise classification},
keywords = {Credal Decision Trees},
keywords = {Ensembles},
keywords = {Bagging},
keywords = {Combination technique},
title = {Upgrading the Fusion of Imprecise Classifiers},
doi = {10.3390/e25071088},
author = {Moral García, Serafín and Benítez, María D.},
}