<p>The number of remaining features after using Cramer’s coefficient to exclude non-essential features.</p
<p>The results of feature valuation on original 31 features using F-score method.</p
The details of the features processed by three levels of feature selection methods.</p
<p>The input features used for RFMQA are listed along with their importance estimates.</p
<p>The numbers of remaining features and their percentages after each data filter.</p
<p>Features sorted by the percentage of missing values, with the two “knees” chosen as thresholds fo...
<p>The number of features in the optimal feature set for each dataset and the MCC values obtained by...
Number of all features detected compared to the number of features within isolation windows, by mini...
<p>Feature groups used in the analysis, and the number of features after pre-selections.</p
<p>The feature dimension of the sparse feature subsets and the full features.</p
<p>The known populations included or excluded combined with graphical analysis of residuals and back...
<p>The number of edges which are persistent and non-persistent according to various metrics.</p
Number of features detected in isolation windows and identified versus minimum voxel intensity.</p
<p>On the right, the corresponding parameter for each feature. For the sake of clarity, only 10 feat...
A list of all 64 variables used in the features selection along with the best features of size 15 an...
<p>On the left: the number of active features equals 5; in the center: the number of kept parameters...
<p>The results of feature valuation on original 31 features using F-score method.</p
The details of the features processed by three levels of feature selection methods.</p
<p>The input features used for RFMQA are listed along with their importance estimates.</p
<p>The numbers of remaining features and their percentages after each data filter.</p
<p>Features sorted by the percentage of missing values, with the two “knees” chosen as thresholds fo...
<p>The number of features in the optimal feature set for each dataset and the MCC values obtained by...
Number of all features detected compared to the number of features within isolation windows, by mini...
<p>Feature groups used in the analysis, and the number of features after pre-selections.</p
<p>The feature dimension of the sparse feature subsets and the full features.</p
<p>The known populations included or excluded combined with graphical analysis of residuals and back...
<p>The number of edges which are persistent and non-persistent according to various metrics.</p
Number of features detected in isolation windows and identified versus minimum voxel intensity.</p
<p>On the right, the corresponding parameter for each feature. For the sake of clarity, only 10 feat...
A list of all 64 variables used in the features selection along with the best features of size 15 an...
<p>On the left: the number of active features equals 5; in the center: the number of kept parameters...
<p>The results of feature valuation on original 31 features using F-score method.</p
The details of the features processed by three levels of feature selection methods.</p
<p>The input features used for RFMQA are listed along with their importance estimates.</p
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