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Evidence combination based on credal belief redistribution for pattern classification
2019
IEEE transactions on fuzzy systems
Evidence theory, also called belief functions theory, provides an efficient tool to represent and combine uncertain information for pattern classification. Evidence combination can be interpreted, in some applications, as classifier fusion. The sources of evidence corresponding to multiple classifiers usually exhibit different classification qualities, and they are often discounted using different weights before combination. In order to achieve the best possible fusion performance, a new Credal
doi:10.1109/tfuzz.2019.2911915
fatcat:7hk3k44n2ne3rg4oqahgiik62m