Jan 8, 2016 · Considering the challenges of using SVM to learn concepts from large-scale imbalanced datasets, we proposed a new method: Boosted Near-miss ...
In this paper, we proposed the Boosted Near-miss Under-sampling on SVM Ensemble (BNU-SVMs) for concept detection in large-scale imbalanced datasets. In BNU-SVMs ...
Considering the challenges of using SVM to learn concepts from large-scale imbalanced datasets, we proposed a new method: Boosted Near-miss Under-sampling ...
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Boosted Near-miss Under-sampling on SVM ensembles for concept detection in large-scale imbalanced datasets ; 刊名, NEUROCOMPUTING ; 出版日期, 2016-01-08 ; 卷号 ...
An Ensemble of Under-Sampled SVMs or EUS SVMs is proposed and it is found that it outperformed other methods, especially when the number of patterns ...
The experimental results on 20 UCI imbalanced datasets show that two new ensemble algorithms proposed in this paper, i.e., CABagE (which is bagging-style) and ...
The integrated sampling technique combines both over-sampling and undersampling techniques and outperforms individual SVMs as well as several other ...
Commentary: The Problem of Class Imbalance in Biomedical Data - PMC
www.ncbi.nlm.nih.gov › PMC7769929
NearMiss undersamples the majority class by removing patients not readmitted early in an effort to balance the data.
5 days ago · Re-sampling methods pre-process the data set and attempt to balance the numbers of samples belonging to different majority and minority classes ...
Apr 9, 2006 · We propose to combine an integrated sampling technique with an ensemble of SVMs to improve the prediction performance. The integrated sampling ...