Quantifying prediction uncertainty in regression using random fuzzy sets: the ennreg model

HIGHLIGHTS

• who: -Evidence theory et al. from the The quantification of prediction uncertainty in Machine Learning has recently gained a lot of attention (see, e_g, [1]-[4])Whereas most approaches are based on Bayesian inference, other theories of uncertainty, such as DempsterShafer (DS) theory [5], [6], have also proved to be very promising [7], [8]. DS theory of belief functions, also known as theory of belief functions of evidence theory, is a mathematical formalism for reasoning with uncertainty [5], [6], which makes it possible to overcome some limitations of Bayesian inference. This formalism relies on two main components . . .
  

   

  

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