Sentence examples for an optimal ranking from inspiring English sources

In order to show that the proposed approach i.e., MULTIMOORA could also provide an optimal ranking in the selection of PA methods, this study utilizes the MULTIMOORA algorithm which has been compared to the TOPSIS method, in order to present the accuracy of the MULTIMOORA and Entropy MULTIMOORA.

They do not have a goal to find an optimal ranking but the results have a good chance to be close to the optimal ranking.

To determine which set of sedentary activities allowed an optimal ranking of older men and women with regard to sedentary time, Spearman correlation coefficients were calculated for the summed self-reported times of all possible combinations of sedentary activities and total objective sedentary time from accelerometry.

The simulated annealing (SA) algorithm [12] is implemented to seek an optimal ranking order that will provide a total cost which is as close to zero as possible.

The information criterion BIC 3 has chosen an optimal rank equal to 1. ( P_{{hat{e}}} ) is distributed as ( chi_{100}^{2} ), with 1, 5 and 10%% critical values of 135.807, 124.342 and 118.498, respectively.

Kemeny-Optimal Ranking is an optimal rank aggregation approach.

This distance was calculated as a Euclidian distance from an optimal rank of 1 for each of the variables.

The reason being that an optimal rank one approximation is achieved by unbiased estimates of the relevant features.

We found that there is an optimal rank value that can be obtained by systematically testing all possible rank values and distances that define whether two species will form part of the same cluster, based on Linnaean taxonomic levels.

Our results on the importance of fitting an optimal rank in the principal component analysis are supported by earlier studies by Meyer [ 22, 11] and Meyer and Kirkpatrick [ 19].

Given n objects and k permutations of the objects, { π1, π2,…, π k }, a Kemeny optimal ranking of the objects is the ranking π that minimizes a "sum of distances" P = ∑ i = 1 k d (x →, r k → ), where d (x →, r k → ), denotes a distance between a rating vector x → and an "individual" vector r k → based on Kendall's τ rank-correlation.