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However, it is interesting to note that two of the methods which treat classification as a two class problem, generating cut-offs from both normal and abnormal putamenal SBRs (i.e. methods SQ 15 and SQ 17), produced some of the highest accuracy figures, with lower variance and well balanced sensitivity and specificity values.
One observation from Figure 1 is that the full set of reference haplotypes generated some of the highest accuracy levels in this experiment – the black curves almost always lie above the other curves.
For summarizing the results, we define the "best result" in each experiment as that of the highest accuracy, the lowest feature selection level below the level of 200 and the shortest feature length.
The latter is one of the highest accuracy prediction algorithms available [ 86], whereas the former has been observed to be especially useful in identifying binding regions within longer regions of disorder [ 87- 89] and to be much better at identifying such sites as compared to a number of different disorder predictors [ 90].
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This is because we mainly try to optimize the detecting efficiency instead of pursuing the highest accuracy.
Simulations pinpoint demonstration and verification to the high accuracy of the algorithm.
Considering a leave one-out cross-validation approach, the SVM of the flower model achieved the highest accuracy of 75.8%.
The largest probe, twice the diameter of the tumor, achieved the highest accuracy for deep tumors.
However, PCA-fused data produced the highest accuracy of 98.8%.
The highest accuracy of IFS was 83.27% using 263 features.
It had the highest accuracy of the estimated breeding value in both the QM and RM (Table 1).
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