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Second, the model can be implemented using both shrinkage methods as well as variable selection methods.
However, as the conventional per-subcarrier selection method selects antennas independently for each subcarrier, a large number of data symbols may be allocated to some particular antennas.
Here we describe the difference between feature selection and feature extraction methods, and introduce the taxonomy of feature selection methods as well as the characteristics of each method.
Stepwise regression was used to as variable selection method.
The strategy of linear regression followed by variable selection suffered high computational complexity, and different feature selection methods yielded distinct results, which made the problem more confusing.
Thus, it was chosen as their feature selection method.
As an alternative model selection method, we performed manual forward selection using α = 0.05 and selected the same model as with the backward selection method.
The Bayesian information criterion (BIC) was used as a selection method to identify the best fit explaining the origin of the variation per gene expression.
The selection methods were as previously described [ 26].
Model selection was conducted per Akaike information criteria and Bayesian information criteria, which confirmed the final models selected by the stepwise selection method.
There are many selection methods such as roulette wheel selection, random selection, tournament selection, rank selection, and Boltzmann selection [33].
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