<p>The <b>Rank</b> represents the feature’s rank from the most “stable” (top) to the most “unstable” (bottom), the <b>ID</b> is the feature’s numeric identifier in WALS <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055009#pone.0055009-Haspelmath1" target="_blank">[39]</a>, <b>Name</b> is the feature’s full name while <b>Abbr. name</b> is the abbreviated name, and and the feature’s score on the first principal component representing the agreement between all methods and excluding outliers, respectively; – are the loadings on principal components 2, 3 and 4 using all shared features. See text for details.</p
<p>Clustering of top-ranked features and their relative importance. The nodes sizes represent the fe...
<p>The values for each feature were z-scored to put them on the same scale across features, and the ...
Feature weighting or selection is a crucial process to identify an important subset of features from...
<p>This ranking represents the consensus among all 12 datasets as given by the first principal compo...
<p>The stabilities (as relative ranks from 0.0 = most unstable to 1.0 = most stable) of the shared f...
<p>The features (abbreviated names are transparently based on the full WALS names and as for <a href...
We sorted the features by combining ranking of the node impurity and the ranking of the percentage o...
<p>(a) The candidate features are ranked by AUC scores calculated on hg19 Training-A; (b) The candid...
<p>Features common to all three techniques are labeled ‘a’. Features common to two techniques are la...
<p>The distances between methods computed in the 62-dimensional space defined by the relative ranks ...
There are needs for evaluating rank order-based similarity between different classifiers in feature ...
Feature ranking of the machine learning algorithms; a lower number indicates a greater importance.</...
<p><b>ID</b> and <b>Name</b> are as in WALS <a href="http://www.plosone.org/article/info:doi/10.1371...
<p>Points concentrated around the diagonal in the top right corner of plots represent a pair of feat...
<p>For each level, the number of clusters (Clusters), the average, m...
<p>Clustering of top-ranked features and their relative importance. The nodes sizes represent the fe...
<p>The values for each feature were z-scored to put them on the same scale across features, and the ...
Feature weighting or selection is a crucial process to identify an important subset of features from...
<p>This ranking represents the consensus among all 12 datasets as given by the first principal compo...
<p>The stabilities (as relative ranks from 0.0 = most unstable to 1.0 = most stable) of the shared f...
<p>The features (abbreviated names are transparently based on the full WALS names and as for <a href...
We sorted the features by combining ranking of the node impurity and the ranking of the percentage o...
<p>(a) The candidate features are ranked by AUC scores calculated on hg19 Training-A; (b) The candid...
<p>Features common to all three techniques are labeled ‘a’. Features common to two techniques are la...
<p>The distances between methods computed in the 62-dimensional space defined by the relative ranks ...
There are needs for evaluating rank order-based similarity between different classifiers in feature ...
Feature ranking of the machine learning algorithms; a lower number indicates a greater importance.</...
<p><b>ID</b> and <b>Name</b> are as in WALS <a href="http://www.plosone.org/article/info:doi/10.1371...
<p>Points concentrated around the diagonal in the top right corner of plots represent a pair of feat...
<p>For each level, the number of clusters (Clusters), the average, m...
<p>Clustering of top-ranked features and their relative importance. The nodes sizes represent the fe...
<p>The values for each feature were z-scored to put them on the same scale across features, and the ...
Feature weighting or selection is a crucial process to identify an important subset of features from...