Sentence examples for minimum Redundancy method from inspiring English sources

The optimal feature selection procedures are adopted by means of the mRMR (Maximum Relevance Minimum Redundancy) method.

Using mRMR method, we ranked and analyzed the top 500 relevant features to translation rate with Maximum Relevance Minimum Redundancy method.

The Maximum Relevance, Minimum Redundancy method [21] was originally developed by Peng et al. The mRMR program used in this paper was downloaded from the website http://penglab.janelia.org/proj/mRMR.org/proj/mRMR

Next, feature selection and analysis methods, including the Maximum Relevance Minimum Redundancy method (mRMR) [21] and Incremental Feature Selection (IFS) [22] were used to obtain the optimal features to be used for the prediction of deleterious nsSNPs versus neutral ones.

Subsequently, the feature selection and analysis methods, including the Maximum Relevance Minimum Redundancy method (mRMR) [21] and the Incremental Feature Selection (IFS) [22] method, were employed to select the optimal features for the prediction of AMPs versus non-AMPs.

And then, all features were ranked using mRMR (maximum relevance & minimum redundancy) method and an optimal model was rebuilt and evaluated with ten-fold cross validations.

Then, the maximum relevance minimum redundancy (mRMR) method [ 12] and incremental feature selection (IFS) method [ 13, 14] are applied to select the optimal feature for prediction.

Their method was based on dagging, with the maximum relevance minimum redundancy (mRMR) method followed by incremental feature selection (IFS).

In this study, we developed a computational method to predict RB related genes based on Dagging, with the maximum relevance minimum redundancy (mRMR) method followed by incremental feature selection (IFS).

The Maximum Relevance Minimum Redundancy (mRMR) method combining with Incremental Feature Selection (IFS) and Feature Forward Selection (FFS) are then applied for feature selection.

Maximum Relevance, Minimum Redundancy (mRMR) Method was originally developed by Peng et al. to process microarray data [46].