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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.
Secondly, when the number of features (p) is larger than the number of samples (n), lasso methods can select at most n features.
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.
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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.
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].
This method can select the salient features from the source images; however, it is sensitive to noise and artifacts as they intend to have higher contrast.
We showed that, by comparing offshore waveforms, our method can select appropriate tsunami scenarios whose maximum coastal tsunami height distributions are close to those of the pseudo-observation scenario.
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