The main advantage of the probabilistic rough set model is that it provides a unified approach for both deterministic and non-deterministic knowledge representation systems.
Can anyone help me with the definitions of both terms: deterministic classification (knowledge representation) and non-deterministic classification?
Deterministic means that the same input will give the same result in every time to solve the problem with the same method. Every time solve the problem with the same inputs will give the same solution. But non-deterministic will give every time different solution for the same input
You can refer to "Markov Decision Process" concept that represents the situations in which the outputs of a model is partly deterministic and partly stochastic.
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