An integrated algebraic approach is developed to calculate stabilities in multiple decision maker graph models with three levels of preference.
For a rule that appears in multiple decision trees corresponding to the same training data, the most significant p-value is taken.
aStudies included in multiple categories if multiple decisions of different types made.
The results obtained in this study suggest that a decision support tool like TADS that supports multiple decisions in a care pathway can significantly reduce deviations from best practice.
It would be useful to investigate how GP can be combined with other machine learning techniques to deal with multiple decisions in production planning and scheduling.
These details are, for example, time labels in starting and ending multiple decisions and differentiating ad hoc decisions aimed at the same targets from other decisions made at the same time.
A grown tree in a forest is represented by a top-down decision tree, in which multiple decision paths from the root to different leaves go through the tree via various nodes.
In some experiments participants make multiple decisions; this feature facilitates gathering a considerable amount of incentivized data over the course of a compact session.
In most cases, households make multiple decisions to use a variety of fuel sources like firewood, charcoal, biogas, kerosene, electricity, etc. [38, 39].
