<p>The features (abbreviated names are transparently based on the full WALS names and as for <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055009#pone-0055009-g004" target="_blank">Figure 4</a>) sorted by the disagreement between methods (IQR). <b>IQR</b> (interquartile range) and <b>Range</b> (Max - Min) between relative stability ranks as given by all methods.</p
<p>Comparison of different algorithms. The number of iterations in each algorithm is chosen to reach...
<p>(DS = David's scores)</p><p>Individual consistency between ranking methods.</p
<p>Boxplots of the core Canberra distance between lists of selected features obtained using differen...
<p>The stabilities (as relative ranks from 0.0 = most unstable to 1.0 = most stable) of the shared f...
<p><b>ID</b> and <b>Name</b> are as in WALS <a href="http://www.plosone.org/article/info:doi/10.1371...
<p>The <b>Rank</b> represents the feature’s rank from the most “stable” (top) to the most “unstable”...
There are needs for evaluating rank order-based similarity between different classifiers in feature ...
Feature selection is a key step when dealing with high-dimensional data. In particular, these techni...
<p>The distances between methods computed in the 62-dimensional space defined by the relative ranks ...
Feature selection plays an important role in applications with high dimensional data. The assessment...
<p>Comparison of different feature selection methods in terms of <i>HR</i>, <i>Precision</i> and <i>...
Stability of feature selection algorithm refers to its robustness to the perturbations of the traini...
Data mining is indispensable for business organizations for extracting useful information from the h...
Analysis of gene-expression data often requires that a gene (feature) subset is selected and many fe...
<p>Stability of different methods in the between-dataset setting, as a function of the size of the s...
<p>Comparison of different algorithms. The number of iterations in each algorithm is chosen to reach...
<p>(DS = David's scores)</p><p>Individual consistency between ranking methods.</p
<p>Boxplots of the core Canberra distance between lists of selected features obtained using differen...
<p>The stabilities (as relative ranks from 0.0 = most unstable to 1.0 = most stable) of the shared f...
<p><b>ID</b> and <b>Name</b> are as in WALS <a href="http://www.plosone.org/article/info:doi/10.1371...
<p>The <b>Rank</b> represents the feature’s rank from the most “stable” (top) to the most “unstable”...
There are needs for evaluating rank order-based similarity between different classifiers in feature ...
Feature selection is a key step when dealing with high-dimensional data. In particular, these techni...
<p>The distances between methods computed in the 62-dimensional space defined by the relative ranks ...
Feature selection plays an important role in applications with high dimensional data. The assessment...
<p>Comparison of different feature selection methods in terms of <i>HR</i>, <i>Precision</i> and <i>...
Stability of feature selection algorithm refers to its robustness to the perturbations of the traini...
Data mining is indispensable for business organizations for extracting useful information from the h...
Analysis of gene-expression data often requires that a gene (feature) subset is selected and many fe...
<p>Stability of different methods in the between-dataset setting, as a function of the size of the s...
<p>Comparison of different algorithms. The number of iterations in each algorithm is chosen to reach...
<p>(DS = David's scores)</p><p>Individual consistency between ranking methods.</p
<p>Boxplots of the core Canberra distance between lists of selected features obtained using differen...