One of the most salient shortcomings of present-day practice is that most of these classifications focus on quantifying uncertainty while ignoring the qualitative aspects that tend to be decisive in the interface between science and policy.
"Classification with imbalanced datasets" section includes a description for imbalanced classification, focusing on its characteristics, metrics of performance, and standard solutions.
In this section, we review some existing works for scene classification focusing on methods that use features extracted on spatial domain.
The purpose of this study is to evaluate the results of TKA in grade II and III valgus knee deformities (Ranawat classification), focusing on axis correction, by using a lateral parapatellar capsulotomy combined with tibial tubercle osteotomy.
We collected binding specificity data (PFMs) from several databases, such as TRANSFAC® (see Table 1), and protein sequences with annotated DNA-binding domains from sequence databases, such as UniProt (see Section 'Protein sequences, DNA-binding domain annotations and TF-classifications') focusing on eukaryotic species.
Specifically, classification focusing on the export apparatus revealed significant details in its overall structure and interaction with the C ring.
Our classification focuses on attacks which directly involve VMs as source and target.
The classification focuses on ALEPH simulated events, with multi-jet topologies.
Another classification focused on usage properties makes the distinction between homogeneous and structured products.
This classification focuses on the anatomical location in the bone affected by the infection and the immunological status of the patient.
This classification focuses on the intra-articular extension of the fracture and the involvement of the ulnar styloid process, implying that this involvement contributes to the seriousness of the fracture [48].
