Nu-support vector regression (Nu-SVR) is employed as modeling method of multi-step-ahead prediction, and a combinational optimization algorithm is proposed to jointly optimize feature selection and hyperparameter selection for prediction model.
Specifically, a semi-supervised manifold embedding is explored to simultaneously optimize feature representation and classifier learning to make the learned binary codes optimal for classification.
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Additionally, the shape overlap optimization does not consider feature atoms, meaning a small change in rotation or translation may either increase or decrease the CT value, considering no attempt was made to optimize feature alignment.
The Relief method is also supervised, but does not optimize feature sets directly for classifier performance.
To optimize feature detection and discovery, two software packages: Mass Hunter Qualitative Analysis and open-source software XCMS (version 1.38.0) operating in R, which adopted different peak detection and alignment algorithms, were used [ 53].
To optimize feature detection and discovery, two software packages: Mass Hunter Qualitative Analysis and open-source software XCMS (version 1.38.0) operating in R, which adopted different peak detection and alignment algorithms, were used.
However, the network data has its inherent complexity of and the combine optimizes features of biological networks alignment are not clear, which make relevant algorithm study extremely challenging.
Furthermore, followed by the non-search feature selection strategy, optimized feature matrix was obtained through the procedure of mean analysis, variation coefficient analysis, cluster analysis and correlation analysis.
As distinct from the general idea of optimizing feature subsets for classification oriented filter and warper models, here we focus on developing an approach to determine relevant features from a training set solely by calculating their individual inter-class discriminatory ability.
