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Upgrading the Fusion of Imprecise Classifiers
dc.contributor.author | Moral García, Serafín | |
dc.contributor.author | Benítez, María D. | |
dc.date.accessioned | 2023-10-04T08:23:23Z | |
dc.date.available | 2023-10-04T08:23:23Z | |
dc.date.issued | 2023-07-19 | |
dc.identifier.citation | Moral-García, S.; Benítez, M.D.; Abellán, J. Upgrading the Fusion of Imprecise Classifiers. Entropy 2023, 25, 1088. [https:// doi.org/10.3390/e25071088] | es_ES |
dc.identifier.uri | https://hdl.handle.net/10481/84822 | |
dc.description.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. | es_ES |
dc.description.sponsorship | UGR-FEDER funds under Project A-TIC-344-UGR20 | es_ES |
dc.description.sponsorship | FEDER/Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades” under Project P20_00159 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.rights | Atribución 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Imprecise classification | es_ES |
dc.subject | Credal Decision Trees | es_ES |
dc.subject | Ensembles | es_ES |
dc.subject | Bagging | es_ES |
dc.subject | Combination technique | es_ES |
dc.title | Upgrading the Fusion of Imprecise Classifiers | es_ES |
dc.type | journal article | es_ES |
dc.rights.accessRights | open access | es_ES |
dc.identifier.doi | 10.3390/e25071088 | |
dc.type.hasVersion | VoR | es_ES |