(2001) elegantly demonstrated that at least some standard laboratory strains might have to be considered 'sick' compared with wild animals, due to the implicit selection regime applied during stock maintenance, driving them towards early reproduction and short lifespan.
The most frequently cited explicit or implicit selection criteria were availability of patient samples (43 articles), covariate data (12), clinical follow-up (7), disease/disease stage (5), treatment (3), level of primary marker (3) and comorbidity (2).
When considering patients' sociodemographic characteristics, a mixed picture of referral patterns emerged over time, suggesting complex, implicit referral selection processes, or even patient 'cherry picking' (ie, selection of high-functioning patients) by referring physicians, may be at play.
Nevertheless, the partial ordering of nodes according to SPV and the implicit model selection in the underlying regressions is a very different procedure in comparison to the standard variable selection approaches, in which the increase or decrease of the R2 is taken as indicator of whether or not a variable is to be included, or a decomposition of R2 is sought [for a review see, e.g., [ 32]].
The implicit material selection knowledge is represented as a set of labeled instances and RDF instance graphs in terms of the concept model, which provides a formal approach to organizing the captured material selection knowledge.
The outcome of this implicit feature selection of the Random Forest can be visualized by the "Gini importance".
The Random Forest model performs an implicit feature selection, using a small subset of "strong variables" for the classification only, leading to its superior performance on high dimensional data.
There has been a long history of domestication and implicit genetic selection; however, systematic breeding programmes have only been used over the last 60 years.
As an implicit feature selection algorithm, derivative component analysis examines input proteomics data in a multi-resolution approach by seeking its derivatives to capture latent data characteristics and conduct de-noising.
The choice of decision tree rather than other popular algorithms such as support vector machines (SVM) is because of the relative parameter-free robust performance and the implicit feature selection provided by the former method, which makes it ideal for evaluating and comparing the performance of the MIL-based and SIL-based approaches.
The random selection implicit in a lottery meant that improvements in the lives of those who moved, compared with the control group, were not a result of superior personal characteristics that had compelled them to move in the first place.
