Design of fuzzy rule-based models with fuzzy relational factorization

Conflict management is an important topic when dealing with unreliable sources information fusion in Dempster–Shafer theory. Discounting unreliable bodies of evidence has proven to be effective to decrease conflict. Based on the generalization of belief functions, a new generalization-based discounting method is proposed. When resolving conflicts with the same degree, our method can realize less information loss in comparison with other discounting methods. By simulating the process of resolving conflicts of randomly generated bodies of evidence, using entropy measurements and binary conflict as evaluation index, we show our method’s rationality and superiority. Finally, the hyperparameters of the conflict metrics are generated and generalization-based discounting is applied to classify real-world datasets. The improved classification performance further illustrates the usefulness of the method.

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Uncertainty Measure-Based Incremental Feature Selection For Hierarchical Classification

Tian,

She

2024

Int. J. Fuzzy Syst.

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Design of fuzzy rule-based models with fuzzy relational factorization

Cited by 10 publications

References 29 publications

Structure identification of missing data: a perspective from granular computing

Structure identification of missing data: a perspective from granular computing

Evidence generalization-based discounting method: assigning unreliable information to partial ignorance

Uncertainty Measure-Based Incremental Feature Selection For Hierarchical Classification

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