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- W4384829950 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." @default.
- W4384829950 created "2023-07-21" @default.
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- W4384829950 date "2023-07-19" @default.
- W4384829950 modified "2023-09-26" @default.
- W4384829950 title "Upgrading the Fusion of Imprecise Classifiers" @default.
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- W4384829950 doi "https://doi.org/10.3390/e25071088" @default.
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