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The Random Forest classifier is a machine learning method that combines decision trees with ensemble learning.
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We propose a step-by-step approach that combines decision-making analysis with a modeling approach inspired by cognitive sciences and software-development methods.
We extend the related literature in restaurant physical environment design and construct a Multiple Criteria Decision-Making (MCDM) model that combines Decision-Making Trail and Evaluation Laboratory DEMATELL) and Analytic Network Process (ANP) to demonstrate the interactions and relations among the criteria.
We also introduced a novel ensemble technique for causal orientation that combines decisions of individual causal orientation methods.
The authors also introduced a novel ensemble technique for causal orientation that combines decisions of individual methods.
Second, we described a novel ensemble technique for causal orientation that combines decisions of individual causal orientation methods to provide a more powerful predictor of causal directionality.
In 1997, an approach was stated to combine decision theory and CBR.
Combining decision support tools with recent computer visualization techniques may be a workable option.
Implementing efficient algorithms for combining decision procedures has been a challenge and their correctness precarious.
In this approach, we combine decision analysis techniques and single-sided auction mechanisms in order to procure goods and services.
In this paper, we present a new approach to combine decision procedures and propositional solvers into an SMT-solver.
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