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In this section, we first analyze the supervised approaches based on big data technologies, and later we compare the best supervised solution with the classical unsupervised methods.
In general, the ROS (RS: 100%) + SVM-BD (regParam: 0.5) classifier can be considered the best supervised solution considering both performance and time.
Tables 11, 12, and 13 from the Appendix list the performance of the best supervised classifiers (marked by bold font in Table 7) when using, respectively, perfusion, diffusion, or spectroscopy data separately, considering imputed features only.
The Bootstrap confidence scores have been shown to be more accurate than any other scoring method and the Bootstrap complexes have the same quality as the best supervised predictions.
Tables 8, 9, and 10 from the Appendix list the performance of the best supervised classifiers (marked by bold font in Table 7) when using, respectively, perfusion, diffusion, or spectroscopy data separately, considering complete features only.
The average results of AUC and G-Mean obtained for the best supervised algorithms and the unsupervised algorithms with different parameter values are shown in Table 12 for Experiments 1 and 2. The average TPRate and TNRate are also depicted in Figure 3.
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But even in the best-supervised labs, conflicts will arise.
The report is the latest in a series of investigations that have found that conflicts of interest in academic research are at best lightly supervised.
Sometimes the font used for text is tricky to read — which is also why it's an app that probably works best under supervised use.
Therefore, we considered BEST a supervised learning tool.
Table 4 shows how the best 6 supervised classifiers (marked by bold font in Table 7) perform on complete features when using each MR modality separately.
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Justyna Jupowicz-Kozak
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