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The performance is evaluated by root mean squared error representing the accuracy, rank error representing the suitability of metamodels when coupled with evolutionary optimization algorithms, training time representing the efficiency and variation of root mean squared error representing the robustness.
When using that algorithm with the above protocols, including the three image gallery training process, the V1-like algorithm achieves 97.5% accuracy (rank one recognition) on FERET240 and 41.3% on LFW610.
Fourth, ASSESS achieved the second highest accuracy rank in both validations.
Third, Mean CORGs achieved a medium accuracy rank in both validations.
First, on average, Mean top 50% achieved the highest accuracy rank in both the external validation (average rank of 1.7) and the internal validation (average rank of 1.0).
For example, although PLS ranked consistently near the bottom in the internal validation, it showed a steady decrease in accuracy rank in the external validation as more pathways are added to the classifiers, yielding a medium average rank of 3.3.
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The study showed that under dark and dim environments, the TMOs' accuracy ranking obtained was different than that for the bright lighting level environments.
It is interesting though that for both ANN and SAM the 30-band cases have more unclassified pixels than either the 194- or the 13-band cases and that the exclusion of the unclassified test pixels from the accuracy calculation results in a reversal of the accuracy ranking between the 13- and the 30-band maps.
Accuracy ranked from 73%to82%2% in main variables.
In the internal validation, Mean top 50% achieved the highest accuracy ranks across all feature set sizes, whereas PCA achieved the lowest accuracy ranks.
In the external validation, there were considerable fluctuations in the combined accuracy ranking.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com