Sentence examples for high cost values from inspiring English sources

The results for cost-sensitive learning with MetaCost using varying misclassification costs are shown in Tables  7 and 8. Classifiers do not seem to be very sensitive to the misclassification cost so performance variations were observed at relatively high cost values.

Our results also indicate, in line with the general conclusion of previous models that spatial structure favours cooperation [ 6, 8, 19, 79], as high values of cooperation evolve for both public actions (hunting and group defense, see large red bubbles) even at high cost values.

The higher cost values are achieved for the exponential distributed service time as compared to gamma distributed service time.

However, spatial population structure promotes cooperation and it allows cooperators to invade at higher cost values with regard of both types of costs (i.e. compare 5.e with 5.g and 5.f with 5.h).

Also for higher cost values the instable fix points move closer to the stable fix points, which means that the invasion of cooperators in a population of defectors becomes harder, while the invasion of defectors into a cooperative population becomes more likely.

Thus the highest cost value 9 represents a high cost requirement.

This means that they achieve the criteria more efficiently that those with a higher cost-value ratio, and therefore represent better value for money.

Indications with high cost-effectiveness values were more likely to be reimbursed (ρ = −0.68, P = 0.001).

We found that indications with high cost-effectiveness values (lower ICERs) were more likely to be reimbursed, which suggests that each country considers cost-effectiveness in addition to clinical efficacy when deciding the reimbursement of a certain drug.

Given that the variation in prices between resource types was comparable to the variation within each type, we decided not to differentiate between resources, but to use mid-point, high and low cost values as inputs to the model.

Currently, energy storage industry in China is still facing with challenges of lack of policy support, high cost, unclear application value, unhealthy market mechanism and other issues.