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The proposed selection method can select the relays, whose instantaneous reward is at least the same as the expected sum reward.
Comparing with several state-of-the-art feature selection methods, our Linear Programming based Feature Selection (LPFS) method can select a small set of features by applying strong regularization while keeping high accuracy.
Comparative studies with peer-reviewed results and two other well-known feature selection methods demonstrate that the proposed method can select dominant features in processing ECG signal, and achieve better classification performance with lower feature dimensionality.
The agreement between simulation and experimental CI2 folding rates, CI2 structural perturbation, and polyglutamine insert structure show that this host-guest method can select a physically realistic model for inserted polyglutamine.
Based on RD competition, the proposed method can select the best coding mode and GOP size prior to actual encoding.
Although some of the features are correlated, the wrapper method can select the useful feature to the predictor regardless of correlation, as discussed in [26].
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In such situations, the unguarded use of stepwise variable selection methods can select incorrect subsets of items and lead to inflated model performance.
Benefiting from the context information surrounding the features, the proposed methods can select distinctive features effectively for protein interaction article classification.
Although these methods can select smaller set of informative genes, only a small percentage of these so called "informative" genes are biologically relevant as proved by medical experiments.
Secondly, when the number of features (p) is larger than the number of samples (n), lasso methods can select at most n features.
Based on this method, we can select, locate, and pattern an oxide film to have different orientation distributions, and thereby engineer films with the complete property anisotropy in-plane.
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