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The Analytic Hierarchy Process was used to build a decision problem that synthesized economic and technical performance as well as social, human and environmental factors.
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This information can be used to build a decision tree for taking decisions, which is built using the training data mentioned previously.
End users often rely on various combinations of tools and knowledge representations to build a decision.
Assume we intend to build a decision tree with L leaves.
A parameterized problem is a (decision) problem equipped with an additional input called the parameter.
Based on the outcome of those two meetings, the researchers built a decision framework for incentives.
We built a decision tree for analysis as shown in Figure 1.
For this purpose, we build a benchmark problem using the widely-used Digimouse atlas [ 21].
Similar to convergent validation in testing between model corroboration [ 33- 35], the distinguishing feature of parallel validation stems from the absence of a model built around the same decision problem.
We build a multi-objective optimization problem utilizing evolutionary computation.
The approach is to build a forest of decision trees, each built using a different subset of the data for training.
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