Sentence examples for more selection methods from inspiring English sources

Alternatively, the common features from two or more selection methods, defined as an intersection set, can be used for prediction.

If a plot contains one or more individuals, selection methods described below are used to choose no more than one individual from the plot.

In these cases, more sophisticated selection methods such as direct-plug-in (DPI) and cross-validation (CV) should be adopted.

In this experiment the tool was used to choose one subset of descriptors from a group of alternative subsets generated automatically by one or more descriptor selection methods.

We further include more feature selection methods especially developed for SA and compare the performance of our proposed method using NBM, SVM, MEM, and DT classifiers along with statistical analysis to prove that our method is classifier independent.

In the present investigation, more comprehensive selection methods were applied to characterize the interactions.

We used this conservative or more protective selection method because the highest results provide information regarding levels of exposure to biologically available lead.

There are better and more advanced feature selection methods (like lasso type or machine learning approaches).

For patients in the intermediate stage (asymptomatic multifocal HCC without vascular localization or metastasis: BCLC stage B), TACE is considered the standard of care, achieving partial response (PR) in 20 50% of patients and an expansion of median survival for up to 20 months throughout the development of new vector systems (polymers) and more accurate patient selection methods [ 18].

More rigorous feature selection methods (Xue et al. 2004a; Al-Shahib et al. 2005a; Al-Shahib et al. 2005b;), such as recursive feature elimination (RFE) (Guyon et al. 2002), can be applied to the SVM classification of functional classes of proteins to select those descriptors most relevant to the prediction of proteins of a particular class (Guyon et al. 2002; Yu et al. 2003).

The results showed that the variables selection methods could select more significant variables and improve the model performance, especially for the GA-Si-PLS model with the best performance than other variables selection algorithms with RP = 0.9426 and RMSEP = 0.2980.