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Then we tackle the problem as a ranking combination issue.
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In the case of unsupervised learning or when the performance cannot be evaluated (e.g., the location of A is not known), cognitive diversity itself can be used to direct us when to combine (when the cognitive diversity is big enough) or how to combine (use rank combination or score combination) (see [12, 14, 21, 22, and 23]).
In addition, we compute the rank combination by averaging the rank functions (r_P) and (r_Q), to give us the score function of the rank combination, (s_{RC}).
Within the CFA framework [1, 2], system combination is performed either by score or rank combination.
C represents the score combination and D represents the rank combination.
In contrast, the lowest ranked combination of assumptions produced an estimate of 9.2%.
The rank combination (RC) is computed as the average rank for a class label for all classifiers.
These points are considered the optimal score and rank combination, respectively, and are used for evaluation of the combination result.
Similar to the results in [12], the six trials do have better rank combination (D) than score combination (C).
In other cases, whenever score combination (C) improves P and Q, rank combination (D) can also improve.
When we sort s D (d i ) in ascending order, we obtain the rank function of the rank combination, called r D (d i ).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com