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Table 1 Comparison of uncertainty strategies

From: Uncertainty in big data analytics: survey, opportunities, and challenges

Uncertainty models Features
Probability theory
Bayesian theory
Shannon’s entropy
Powerful for handling randomness and subjective uncertainty where precision is required
Capable of handling complex data [50]
Fuzziness Handles vague and imprecise information in systems that are difficult to model
Precision not guaranteed
Easy to implement and interpret [50]
Belief function Handle situations with some degree of ignorance
Combines distinct evidence from several sources to compute the probability of specific hypotheses
Considers all evidence available for the hypothesis
Ideal for incomplete and high complex data
Mathematically complex but improves uncertainty reduction [50]
Rough set theory Provides an objective form of analysis [47]
Deals with vagueness in data
Minimal information necessary to determine set membership
Only uses the information presented within the given data  [51]
Classification entropy Handles ambiguity between the classes [39]