Sentence examples for decision making instances from inspiring English sources

Finally classification decision (for N > 1) is made by the majority voting from the class labels of those nodes after each node has already made its own decision based on majority voting from the decision making instances in each node.

It appears that the performance of the IUP predictor depends on several parameters, including the window length L for feature generation, the number of decision making instances K inside a RMCT tree node and the number of tree nodes N that are used for decisions.

We performed the experiments to test how the number of decision-making instances affects the performances of IUP prediction.

As shown in Table 3, The classifier achieved the best performance in detecting IUP regions when the number of decision-making instances K equals to 9.

We sort the distances in ascending order and get the first K minimum distances as our K decision-making instances in a tree node.

Therefore, adjustable parameters in the IUP predictor are the window length L (used in generating features), the number of decision-making instances K in each node on RMCT and number of tree nodes participating in final classification decision (N).

Experimental results indicate that N is least sensitive and L is most sensitive, therefore, we focused on a series of experiments using a variety of different window L lengths L and decision-making instances K.

In addition, to further complicate matters, it may turn out that some of the most useful records for future instances of decision making are instances of medical errors or other unexpected events that are unique in their course of events and therefore easily identifiable together with their contexts of development (i.e., patients, providers, family members).

Leaders I have studied use it to describe how they think about employee decision making, for instance, or how they look at the central organization's relationship to business units or individual brands.

Automation tools can also enable better on-the-spot decision making, for instance showing your sales teams how discounting a certain product line will impact your profitability or how likely customer segment A will respond to a 15% discount.

Cross-sectional analysis by NGS-derived phenotypes and risk factors in the EMR would facilitate more precise clinical decision making, for instance, whether shortening patient time in intensive care units or decreasing use of provocative antibiotics would be more preventive within the local milieu.