Item identification, classification, selection, and initial item revision were completed by project investigators (BB, JFF, DA, BG).
The overall process consists of five components, that is, data reduction, distance-based hierarchical clustering, Bayesian integration and classification, selection of biomarker candidates, and validation.
Quantitative comparisons among studies were precluded by differences in tumor staging classifications, surgical selection, CT and EUS techniques, and reporting of operating characteristics.
A maximum estimability (maxest) criterion is proposed for design classification and selection.
This kind of retrospective virtual screening is performed by using encoded molecule data and a neuro-fuzzy methodology for classification, feature selection, and rule generation.
When the SVM is used for classification, the selection of the kernel function and the determination of the corresponding parameters become crucial.
The aggregation memberships, the classification, the selection of aggregators are all complexity hidden from the aggregation process of the top level.
These multi-criteria decision analysis methods are used as tools for the classification and selection of the best alternative amongst a set of options.
Basic design carries out the identification, classification and selection of constraints and then determines the design architecture of how the system obtains its specification.
Prior to multivariate classification, feature selection was performed on the data from the training set.
