Decision forest method

The Decision Forest (DF) is a DT-based consensus modeling method that was conceived as a method for combining heterogeneous yet comparable trees that fully captures the association between molecular structure and biological activity (Figure 6.4) [59], The heterogeneity requirement assures that each tree uniquely contributes to the combined prediction whereas the quality comparability requirement assures that each tree equally contributes to the combined prediction. 

Hong H, Tong W, Xie Q, Fang H, Perkins R. An in silico ensemble method for lead discovery decision forest. SAR QSAR Environ Res 2005 16(4) 339-347. 

Ho, T. K. (1998). The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Machine IntelL, 20 832-844. 

Alternatively, multiple models can be developed using different sets of descriptors [57], One popular decision tree (DT) consensus method, called Random Forests, has recently demonstrated improved performance over bagging [58], The DT method determines a chemical s activity through a... 

R0yne, F., Penaloza, D., Sandin, G., Svanstrom, M., Berlin, J. Climate impact assessment in life cycle assessments of forest products Implications of method choice for results and decision-making. Journal of Cleaner Production, in press. 

The increasing use of Probabilistic Safety Assessment (PSA) to support regulatory and operational decisions requires that methods be developed to the extent possible on an empirically sound basis. For Human Reliability Analysis (HRA), this means that methods should be based on data from operational experience, studies in simulator environments, as well as theoretical cognitive models. A significant step in this direction has been achieved with the International and US HRA Empirical Studies, which aimed at an empirically-based understanding of the strengths and weaknesses of a number of HRA methods (Forester et al. 2013). 

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