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An alternative hypothesis for explaining phenotypic variability was that Scn5a+/− mice had variable reduction in Na+ current.
Variable reduction and mtry optimization resulted in minimal improvements in RF predictions.
Molecular descriptors calculated by E-Dragon and RECON were subjected to variable reduction using the Unsupervised Forward Selection (UFS) algorithm.
Unsupervised variable reduction methods are intended for reducing the presence of redundancy and multicollinearity in the data.
It is then necessary to estimate their multipliers, and variable reduction techniques can be used to carry out the successive minimizations.
Finally, the application studies demonstrate the internal consistency of the Latent Variable reduction coefficients, which overturns previous results and paves the way for further research into undesirable externalities.
In this study, we develop an ad hoc iterative feature (variable) reduction algorithm for basic PNNs to identify noise and redundant variables.
A Monte Carlo simulation is further applied to generalize and compare two discrimination improving methods: (a) PCA applied to DEA, and (b) variable reduction based on partial covariance.
In particular, the LDR air interface can increase its goodput to acceptable values with a variable reduction of the HDR duty cycle (from 6.25% to 50%).
Because of the overwhelming number of variables and for the purpose of variable reduction, factors that explain the least amount of variance will be discarded.
In the first part of this review we reported an overview on these procedures and described the applications of chemometric uni- and bivariate techniques and of multivariate pattern recognition techniques based on variable reduction to the experimental results obtained.
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