<p>The average ranks of the Friedman test for the seven different classifiers using the additive encoding. (Small values are better.) The result of the Friedman test over all data sets is significant (<i>p</i> < 10<sup>−15</sup> for <i>k</i> = 7, <i>n</i> = 42). The table also shows the average ranks for each data set separately, but the Friedman test is not applicable here because the number of treatments is bigger than the number of problems (<i>k</i> = 7, <i>n</i> = 6).</p><p>Average ranks of the seven classification algorithms.</p
<p>Friedman’s rank test for the 180 instance combination of the benchmarked problem of ACO, ACOII, S...
Comparison of the average classification accuracies of the proposed algorithm for different numbers ...
<p>Ranking of different algorithms with respect to the median AUC in a 10 times repeated 10-fold cro...
<p>The average ranks are computed from absolute errors, measured by <i>Alpha</i>. The black bars con...
<p>The number in the parenthesis corresponds to the average rank of the algorithm among different su...
<p>The number of subjects in binary task was 12 and the number of subjects in multi-task BCIs was 9....
<p>Comparison of the average classification accuracies of different algorithms for different numbers...
<p>Rankings of the algorithms by the Friedman’s test on the basic benchmark functions.</p
<p>Results of a Friedman test to compare prediction methods across different datasets and feature se...
<p>Results of a Friedman test to compare feature selection methods in terms of classification accura...
<p>The seven classification algorithms compared by their rank distance over all disease data sets us...
<p>Friedman’s rank test for the 120 instance combination of the benchmarked problem of GADP, SADP, S...
<p>Ranks assigned to the performances of 6 classifiers by 17 activity classes from <a href="http://w...
<p>Rankings of the algorithms by the Friedman’s test on the CEC2005’s shifted functions.</p
<p>Differences between the average ranks of the six algorithms and the average rank of LRSE+SC.</p
<p>Friedman’s rank test for the 180 instance combination of the benchmarked problem of ACO, ACOII, S...
Comparison of the average classification accuracies of the proposed algorithm for different numbers ...
<p>Ranking of different algorithms with respect to the median AUC in a 10 times repeated 10-fold cro...
<p>The average ranks are computed from absolute errors, measured by <i>Alpha</i>. The black bars con...
<p>The number in the parenthesis corresponds to the average rank of the algorithm among different su...
<p>The number of subjects in binary task was 12 and the number of subjects in multi-task BCIs was 9....
<p>Comparison of the average classification accuracies of different algorithms for different numbers...
<p>Rankings of the algorithms by the Friedman’s test on the basic benchmark functions.</p
<p>Results of a Friedman test to compare prediction methods across different datasets and feature se...
<p>Results of a Friedman test to compare feature selection methods in terms of classification accura...
<p>The seven classification algorithms compared by their rank distance over all disease data sets us...
<p>Friedman’s rank test for the 120 instance combination of the benchmarked problem of GADP, SADP, S...
<p>Ranks assigned to the performances of 6 classifiers by 17 activity classes from <a href="http://w...
<p>Rankings of the algorithms by the Friedman’s test on the CEC2005’s shifted functions.</p
<p>Differences between the average ranks of the six algorithms and the average rank of LRSE+SC.</p
<p>Friedman’s rank test for the 180 instance combination of the benchmarked problem of ACO, ACOII, S...
Comparison of the average classification accuracies of the proposed algorithm for different numbers ...
<p>Ranking of different algorithms with respect to the median AUC in a 10 times repeated 10-fold cro...