<p>SVM-REF ranked the features according to their ability to separate different categories for each dataset. So the ranking lists and top features are different for different datasets. Apparently, proportions of different kinds of features are consistent for all seven datasets, i.e., physical-chemical properties reflected by PROFEAT constitute the majority group, followed subsequently by PSSM and GO annotation features. The bar chart shows the numbers of three different kinds of features in top features for each dataset.</p
<p>Metrics included are participation index (PI), local/global efficiency (LE/GE), local efficiency ...
<p>Distribution of the top 1% connections contributing to correct SVM classification for (A) S vs. W...
In computational chemistry and chemoinformatics, the support vector machine (SVM) algorithm is among...
<p>After feature selection by SVM-REF, 157 and 155 PROFEAT features are selected in top322 features ...
Ranked accuracies from the support vector machine analysis described in the paper for all of the cla...
<p>Gray dotted lines highlight the selected top features for high (top 70) and low (top 322) similar...
<p>The solid bars, checked bars, gray bars, light gray bars and hatched bars represent features of P...
<p>List of 10 most discriminative features of four selected families for SVM-PDT. The features are s...
<p>For each of the top 20 ranked features (ID stands for the feature ...
<p>If a single feature is used, feature 4 (CV of spike width) is best for data set 1 (A) and feature...
<p>(A) Distribution of scores of the SVM method. The vertical line indicates the cutoff for the sele...
<p>The ranking was obtained using the SVM attribute evaluating protocol ...
<p>The figure shows the error rate of SVM vs. top ranked discriminant time-frequency features. Simil...
Abstract. A relaxed setting for Feature Selection is known as Feature Ranking in Machine Learning. T...
<p>Rank and feature scores obtained after performing feature selection show the structural profile m...
<p>Metrics included are participation index (PI), local/global efficiency (LE/GE), local efficiency ...
<p>Distribution of the top 1% connections contributing to correct SVM classification for (A) S vs. W...
In computational chemistry and chemoinformatics, the support vector machine (SVM) algorithm is among...
<p>After feature selection by SVM-REF, 157 and 155 PROFEAT features are selected in top322 features ...
Ranked accuracies from the support vector machine analysis described in the paper for all of the cla...
<p>Gray dotted lines highlight the selected top features for high (top 70) and low (top 322) similar...
<p>The solid bars, checked bars, gray bars, light gray bars and hatched bars represent features of P...
<p>List of 10 most discriminative features of four selected families for SVM-PDT. The features are s...
<p>For each of the top 20 ranked features (ID stands for the feature ...
<p>If a single feature is used, feature 4 (CV of spike width) is best for data set 1 (A) and feature...
<p>(A) Distribution of scores of the SVM method. The vertical line indicates the cutoff for the sele...
<p>The ranking was obtained using the SVM attribute evaluating protocol ...
<p>The figure shows the error rate of SVM vs. top ranked discriminant time-frequency features. Simil...
Abstract. A relaxed setting for Feature Selection is known as Feature Ranking in Machine Learning. T...
<p>Rank and feature scores obtained after performing feature selection show the structural profile m...
<p>Metrics included are participation index (PI), local/global efficiency (LE/GE), local efficiency ...
<p>Distribution of the top 1% connections contributing to correct SVM classification for (A) S vs. W...
In computational chemistry and chemoinformatics, the support vector machine (SVM) algorithm is among...