Uncertainty measures of rough set prediction. Artif Intell 1998;106(1):109-37.Duentsch, I., Gediga, G.: Uncertainty measures of rough set prediction. Artificial Intelligence 106 (1998) pp. 77-107.I. Duntsch, G. Gediga, Uncertainty measures of rough set prediction, Artificial Intelligence ...
Duentsch, I., Gediga, G.: Uncertainty measures of rough set prediction. Artif. Intell. 106, 109–137 (1998) Article MathSciNet MATH Google Scholar Euler, L.: Solutio problematis ad geometriam situs pertinentis, Commentarii Academiae Scientiarum Imperialis Petropolitanae 8:128–140 (1736) Fa...
Shannon entropy [24] provides a measure of uncertainty for probability theory that has been used in many fields. Similarity, fuzzy sets and rough sets also feature their own uncertainty measures Although a number of methods exist for measuring BPA uncertainty, this is a challenging subject and ...
Examining all possible combinations of these two factors, we derive a set of predictive uncertainty measures that includes both known and newly introduced ones. We empirically evaluate these measures in typical uncertainty estimation settings, such as misclassification detection, selective prediction, and ...
[34] used distance measures of Manhattan distance, and Yuan et al. [26] employed a hybrid kernel function to compute the fuzzy similarity relations, which could surmount the discrimination bottleneck encountered in the intersection operations. Kernel functions such as the linear kernel [35], ...
Based on these results, we sought to assess whether similar relationships between computational measures of uncertainty and impulsivity were observed in a population of persons with PD undertaking subthalamic DBS. Moreover, given the central role of this brain region in decision-making, we investigated...
“normal” outcomes. This gives us two contrasting, but complementary measures of uncertainty: width of range (wide vs. narrow) and associated degree of confidence (low vs. high). Formally, these measures are inversely related, so a narrow interval that contains, for instance, 60% of the ...
The MOAT mean estimates the overall effect of an input parameter on the QoI, and the MOAT standard deviation measures the nonlinear effects of the input parameter and the interaction effect of this input parameter and others. Note that the choice of n is strictly linked to the choice of r....
These measures of uncertainty allow to perform risk analysis in reservoir optimization problems, a subject of paramount importance due to the huge economic investments that are needed in the petroleum industry. This methodology could be used to optimize real petroleum fields by using parallel computing...
It is also important to note that the return of investment and profit shown are merely measures of merit. The difference in return between simulated markets cannot be taken for granted as there are many factors, like success rate of the shipping company, which will effect the eventual size of...