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Feature Selection via Robust Weighted Score for High Dimensional Binary Class-Imbalanced Gene Expression Data
The performance of the proposed ROWSU method is evaluated on $6$ gene expression datasets.
Optimal Extended Neighbourhood Rule $k$ Nearest Neighbours Ensemble
Each base model is constructed on a bootstrap sample with a random subset of features, and optimal models are selected based on out-of-bag performance after building a sufficient number of models.
A k nearest neighbours classifiers ensemble based on extended neighbourhood rule and features subsets
kNN based ensemble methods minimise the effect of outliers by identifying a set of data points in the given feature space that are nearest to an unseen observation in order to predict its response by using majority voting.
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