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For either option, predictions by different classifiers can be performed individually or hierarchically.
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It is well known that a combination of many different classifiers can improve classification accuracy.
In the future work, different classifiers can be used to increase the accuracy combining more efficient segmentation and feature extraction techniques with real- and clinical-based cases by using large dataset covering different scenarios.
Partly this variation reflects intra-subtype heterogeneity treated differently by different classifiers.
Features selected by different classifiers has minor difference, and results of prediction accuracy are also different.
Figure 6 shows the normalized time taken by different classifiers for testing 14,086 sequences.
However, in different conditions, certain classifiers can deliver a better performance.
Only instances near the boundary are considered, and such difference between two classifiers can lead to different result.
Although cluster purity does not guarantee high recognition performance, from Tables 1 and 2 it can be seen that the modulation spectrum features appear to capture substantial information that can be exploited by two very different classifiers.
That is, for fixed (w, b) parameters, each value of the threshold τ defines a classifier; we can therefore consider a whole range of different classifiers, by adjusting this τ value.
The authors showed that classifiers can have different accuracies for each embryo component (blastocyst extension, ICM, and TE).
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