<p>Features sorted by the percentage of missing values, with the two “knees” chosen as thresholds for feature selection.</p
The degree of missingness (%) in different baseline data modalities for two patient groups.</p
<p>Comparison of the classification results of original and selected features.</p
Abstract. Feature selection is a process followed in order to improve the generalization and the per...
<p>The numbers of remaining features and their percentages after each data filter.</p
<p>The number of remaining features after using Cramer’s coefficient to exclude non-essential featur...
<p>On the right, the corresponding parameter for each feature. For the sake of clarity, only 10 feat...
The details of the features processed by three levels of feature selection methods.</p
<p>Selected optimal feature subsets for each level with backward elimination.</p
<p>On the left: the number of active features equals 5; in the center: the number of kept parameters...
Feature ranking of the machine learning algorithms; a lower number indicates a greater importance.</...
We sorted the features by combining ranking of the node impurity and the ranking of the percentage o...
<p>Feature groups used in the analysis, and the number of features after pre-selections.</p
<p>Percentage of errors in each condition, with target-present and target-absent trials listed separ...
Subject-wise two-class classification of positive and negative displays based on best feature combin...
<p>Comparison of accuracy rate of different features extracted with classification algorithms.</p
The degree of missingness (%) in different baseline data modalities for two patient groups.</p
<p>Comparison of the classification results of original and selected features.</p
Abstract. Feature selection is a process followed in order to improve the generalization and the per...
<p>The numbers of remaining features and their percentages after each data filter.</p
<p>The number of remaining features after using Cramer’s coefficient to exclude non-essential featur...
<p>On the right, the corresponding parameter for each feature. For the sake of clarity, only 10 feat...
The details of the features processed by three levels of feature selection methods.</p
<p>Selected optimal feature subsets for each level with backward elimination.</p
<p>On the left: the number of active features equals 5; in the center: the number of kept parameters...
Feature ranking of the machine learning algorithms; a lower number indicates a greater importance.</...
We sorted the features by combining ranking of the node impurity and the ranking of the percentage o...
<p>Feature groups used in the analysis, and the number of features after pre-selections.</p
<p>Percentage of errors in each condition, with target-present and target-absent trials listed separ...
Subject-wise two-class classification of positive and negative displays based on best feature combin...
<p>Comparison of accuracy rate of different features extracted with classification algorithms.</p
The degree of missingness (%) in different baseline data modalities for two patient groups.</p
<p>Comparison of the classification results of original and selected features.</p
Abstract. Feature selection is a process followed in order to improve the generalization and the per...
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