<p>Rows contain labels assigned by the majority of classifiers trained with the PAM50 list, while columns contain the the original METABRIC labels assigned using the PAM50 method. In this table, <i>LA</i> corresponds to luminal A, <i>LB</i> corresponds to luminal B, <i>H</i> to HER2-enriched, <i>N</i> to normal-like, and <i>B</i> to basal-like. Labels marked as <i>I</i> refer to inconsistent assignments; situations where the classifiers did not achieve the majority on attributing a subtype label.</p><p>Contingency tables for predicted labels using the 24 classifiers trained with the PAM50 list.</p
Most of the multi-label classification (MLC) methods proposed in recent years intended to exploit, i...
Most of the multi-label classification (MLC) methods proposed in recent years intended to exploit, i...
<p>Features common to all three techniques are labeled ‘a’. Features common to two techniques are la...
<p>Rows contain labels assigned by the majority of classifiers trained with the CM1 list, while colu...
<p>Rows contain the labels assigned by the majority of classifiers trained with the CM1 list, while ...
<p>The bars represent the number of samples in each breast cancer subtype. In the first row, the lab...
<p>Rows entitled <i>Among classifiers</i> indicate agreement of classifiers alone, not considering t...
<p>(A) Discovery and (B) Validation. The bars represent the number of samples with <i>HER2</i> ampli...
<p>The image shows the similarity between the subtypes distribution for METABRIC discovery (MD) and ...
<p>(A) Discovery and (B) Validation. The bars represent the number of samples with PR positive and n...
<p>This contains the agreement between the original and predicted labels of samples in the discovery...
<p>(A) Discovery and (B) Validation. The bars represent the number of samples with ER positive and n...
Two fundamental and prominent methods for multi-label classification, Binary Relevance (BR) and Clas...
Abstract. In the “classifier chains ” (CC) approach for multi-label clas-sification, the predictions...
<p><b>(A)</b> The mean prediction accuracy of the classifier for the 100% signal condition in the si...
Most of the multi-label classification (MLC) methods proposed in recent years intended to exploit, i...
Most of the multi-label classification (MLC) methods proposed in recent years intended to exploit, i...
<p>Features common to all three techniques are labeled ‘a’. Features common to two techniques are la...
<p>Rows contain labels assigned by the majority of classifiers trained with the CM1 list, while colu...
<p>Rows contain the labels assigned by the majority of classifiers trained with the CM1 list, while ...
<p>The bars represent the number of samples in each breast cancer subtype. In the first row, the lab...
<p>Rows entitled <i>Among classifiers</i> indicate agreement of classifiers alone, not considering t...
<p>(A) Discovery and (B) Validation. The bars represent the number of samples with <i>HER2</i> ampli...
<p>The image shows the similarity between the subtypes distribution for METABRIC discovery (MD) and ...
<p>(A) Discovery and (B) Validation. The bars represent the number of samples with PR positive and n...
<p>This contains the agreement between the original and predicted labels of samples in the discovery...
<p>(A) Discovery and (B) Validation. The bars represent the number of samples with ER positive and n...
Two fundamental and prominent methods for multi-label classification, Binary Relevance (BR) and Clas...
Abstract. In the “classifier chains ” (CC) approach for multi-label clas-sification, the predictions...
<p><b>(A)</b> The mean prediction accuracy of the classifier for the 100% signal condition in the si...
Most of the multi-label classification (MLC) methods proposed in recent years intended to exploit, i...
Most of the multi-label classification (MLC) methods proposed in recent years intended to exploit, i...
<p>Features common to all three techniques are labeled ‘a’. Features common to two techniques are la...