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To overcome these problems, we use feature selection technique to select a subset of relevant and non-redundant features.
The manual selection technique consists in selecting the initial pairs in the two images manually.
Individual-level variables included as final model covariates were selected using a backward variable selection technique (p for inclusion < 0.05).
The best solutions are selected for crossover and reproduction using a fitness-proportionate selection technique.
We applied the variable selection technique, 10% change-in-estimate criterion (Rothman et al. 2008), to select potential confounding factors.
An efficient input selection technique is crucial for decreasing model data requirements.
Then, a supervised feature selection technique is employed to reduce feature space.
It means that the channel selection technique is reasonable and credible.
In this paper, a feature selection technique using genetic algorithms is applied.
In this paper, an automated low- fidelity model selection technique is presented.
Existing reports do not elucidate trends in patient selection, technique, and outcomes over time.
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