Performances on the DREAM4 Multifactorial subchallenge using improved method.
- Jie Xiong (138164)
- Tong Zhou (3770)
Publication date
February 2013
DOIAbstract
<p>Performances on the DREAM4 Multifactorial subchallenge using improved method.</p
<p>Performances on the DREAM4 Multifactorial subchallenge using improved method.</p
<p>Classification performance achieved with different feature reduction techniques.</p
<p>Performance comparison and relative gain of the proposed scheme over the existing schemes.</p
Estimation performance over the robust panels for cell-subtype and lineage groupings.</p
<p>Reconstruction performance for the DREAM3 and DREAM4 in the size 100 subchallenges.</p
Comparison of the performance of the four methods under the four distributions data.</p
<p>The performance of our method and other existing methods on PDB186 dataset.</p
The performance comparison results for the three different classifiers using four indexes.</p
<p>The performances of the different classification algorithms as a function of the number of trials...
<p>Greater exploration associated with improved performance, across different sets of trials.</p
<p>Classification performance of the single metrics and multi-modal combinations.</p
Comparing performance of the proposed methods built with different number of individual models.</p
<p>Performance measures for 4 unimodal and 6 multimodal decision-level fusions.</p
<p>The performance of different classifiers associated with the attribute selection methods assessed...
<p>Comparing the performance of the proposed method with other existing methods.</p
<p>Evaluation results for different combinations of binarization and learning methods on the four ne...
<p>Classification performance achieved with different feature reduction techniques.</p
<p>Performance comparison and relative gain of the proposed scheme over the existing schemes.</p
Estimation performance over the robust panels for cell-subtype and lineage groupings.</p
<p>Reconstruction performance for the DREAM3 and DREAM4 in the size 100 subchallenges.</p
Comparison of the performance of the four methods under the four distributions data.</p
<p>The performance of our method and other existing methods on PDB186 dataset.</p
The performance comparison results for the three different classifiers using four indexes.</p
<p>The performances of the different classification algorithms as a function of the number of trials...
<p>Greater exploration associated with improved performance, across different sets of trials.</p
<p>Classification performance of the single metrics and multi-modal combinations.</p
Comparing performance of the proposed methods built with different number of individual models.</p
<p>Performance measures for 4 unimodal and 6 multimodal decision-level fusions.</p
<p>The performance of different classifiers associated with the attribute selection methods assessed...
<p>Comparing the performance of the proposed method with other existing methods.</p
<p>Evaluation results for different combinations of binarization and learning methods on the four ne...
<p>Classification performance achieved with different feature reduction techniques.</p
<p>Performance comparison and relative gain of the proposed scheme over the existing schemes.</p
Estimation performance over the robust panels for cell-subtype and lineage groupings.</p
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