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Indicators gleaned from the data collection methods were ranked and verified by expert herders.
On the basis of these results, the computational methods were ranked according to their performances using the multi-criteria decision making procedures preference ranking organization method for enrichment evaluation (PROMETHEE) and geometrical analysis for interactive aid (GAIA).
The methods were ranked with respect to their average across the three criteria.
The methods were ranked considering the area under the sparsification curve (AUS).
For each of the fourteen datasets, six methods were ranked according to their balanced accuracy values.
In brief, accuracies from the six methods were ranked from the highest to the lowest in each of the fourteen datasets.
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Finally, the methods are ranked according to their efficiency as regards to the landscapes and the visual expertises.
The results show that linear logistic, Bayesian Network, and ensemble methods are ranked as the top-three classifiers by TOPSIS, PROMETHEE, and VIKOR.
Another study of the comparison between TOPSIS and fuzzy TOPSIS chose the best facility layout among 18 possible designs (Maniya and Bhatt 2011); the results showed that both methods are ranked first in the alternative ranking.
When evaluated by BEDROC (α = 160.9), Molprint-2D shows the highest value (0.848), and the rest of the methods are ranked in the following order: SIMCOMP, USR (Manhattan, 0.782), MACCS (0.778), and the 3DZD (0.739 with the correlation coefficient, order = 8).
Next, the methods are ranked by error using dense ranking (the 1-2-2-3 1-2-2-3 1-2-2-3 1-2-2-3
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