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##### References 17 publications
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“…Both of these two notions have some desired properties in characterizing the uncertainty for formulae in pre-rough logic, for those reported in Prop.2(1)-(4), (6). However, they have some limitations, to show this, let us consider Prop.2 (5), which states that Rou(A) reaches the value of 1 if and only if τ (A) = 0,τ (A) ̸ = 0, equivalently, in the presence of τ (A) = 0,τ (A) ̸ = 0, whatever valueτ (A) may take, the value of Rou(A) always equals 1, i.e., A is rough to the largest degree 1. This seems not reasonable because it completely ignores the value ofτ (A) when τ (A) = 0.…”
Section: The Second Type Of Accuracy Degree and Roughness Degree Of Fmentioning
confidence: 99%
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“…Both of these two notions have some desired properties in characterizing the uncertainty for formulae in pre-rough logic, for those reported in Prop.2(1)-(4), (6). However, they have some limitations, to show this, let us consider Prop.2 (5), which states that Rou(A) reaches the value of 1 if and only if τ (A) = 0,τ (A) ̸ = 0, equivalently, in the presence of τ (A) = 0,τ (A) ̸ = 0, whatever valueτ (A) may take, the value of Rou(A) always equals 1, i.e., A is rough to the largest degree 1. This seems not reasonable because it completely ignores the value ofτ (A) when τ (A) = 0.…”
Section: The Second Type Of Accuracy Degree and Roughness Degree Of Fmentioning
confidence: 99%
“…Then we call Acc S (A), Rou S (A) the second type of accuracy degree and the roughness degree of A, respectively. It can be checked easily that the second type of accuracy degree and roughness degree for formulae in rough logic satisfy the properties listed in Prop.2 except Prop.2 (5), whose revised form can be given as follows:…”
Section: Definition 10 Let a ∈ F (S) Definementioning
confidence: 99%
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“…, , U A d from which a classifier is to be constructed; for a discussion on the subject of data classification, cf., e.g. [24]; we assume that on the universe U, a rough inclusion μ is given, and a radius r in [0,1] is chosen, consult [8] for detailed discussion. We can find granules ( ) is defined.…”
Section: The Idea Of Granular Rough Mereo-logical Classifiersmentioning
confidence: 99%
“…In Table 7, we give a comparison of performance of rough set classifiers: exhaustive, covering and LEM, cf. [24], implemented in the Rough Set Exploratory System (RSES) due to Skow-ron et al [28]. We begin in the next section with granular classifiers in which granules are induced from the train-ing set, cf.…”
Section: The Idea Of Granular Rough Mereo-logical Classifiersmentioning
confidence: 99%