<p>Classification performance of the single metrics and multi-modal combinations.</p
<p>This indicates the extent that each metric can be used to predict performance.</p
<p>Classification performances of the proposed method according to the number applied base classifie...
<p>Performance measures for 4 unimodal and 6 multimodal decision-level fusions.</p
<p>The performances of the different classification algorithms as a function of the number of trials...
<p>The performance of different classifiers associated with the attribute selection methods assessed...
<p>The classification performances of the 3 best classifiers for the 3 datasets.</p
<p>Performance of different feature combinations for disease detection and quantification.</p
<p>The mean classification performance is shown for each time window and each subject.</p
Performance comparison of different fold combinations based on the five measures.</p
<p>Performance of the “Training Data Set” using the classification algorithm J48.</p
<p>Classification performance of all methods as a function of feature subset size, for a) first b) s...
The performance comparison results for the three different classifiers using four indexes.</p
<p>Classification performances of all methods as a function of feature subset size, for a) first b) ...
<p>Classification performance achieved with different feature reduction techniques.</p
<p>Classification performance of all methods as a function of training set size, for a) first b) sec...
<p>This indicates the extent that each metric can be used to predict performance.</p
<p>Classification performances of the proposed method according to the number applied base classifie...
<p>Performance measures for 4 unimodal and 6 multimodal decision-level fusions.</p
<p>The performances of the different classification algorithms as a function of the number of trials...
<p>The performance of different classifiers associated with the attribute selection methods assessed...
<p>The classification performances of the 3 best classifiers for the 3 datasets.</p
<p>Performance of different feature combinations for disease detection and quantification.</p
<p>The mean classification performance is shown for each time window and each subject.</p
Performance comparison of different fold combinations based on the five measures.</p
<p>Performance of the “Training Data Set” using the classification algorithm J48.</p
<p>Classification performance of all methods as a function of feature subset size, for a) first b) s...
The performance comparison results for the three different classifiers using four indexes.</p
<p>Classification performances of all methods as a function of feature subset size, for a) first b) ...
<p>Classification performance achieved with different feature reduction techniques.</p
<p>Classification performance of all methods as a function of training set size, for a) first b) sec...
<p>This indicates the extent that each metric can be used to predict performance.</p
<p>Classification performances of the proposed method according to the number applied base classifie...
<p>Performance measures for 4 unimodal and 6 multimodal decision-level fusions.</p
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