Sentence examples for decision tree classifications from inspiring English sources
To estimate the proportion of each land cover class for every pixel several decision tree classifications were combined to obtain class membership maps which were finally converted to a discrete map accompanied by a confidence estimate.
Decision tree classification can be improved by combining the predictions of several trees with boosting and bagging techniques.
A decision tree classification analysis across the three modalities resulted in classification accuracy of 91.9% with FA, RD, and cortical thickness as key predictors.
We examined recovery of forest pattern following wildfire events and derived a large-area fire susceptibility model using decision tree classification.
Decision tree classification was applied in conjunction with bootstrap aggregation to gain insight in the distinctive character of the defined metrics, and the robustness of land use and urban form classification based on these metrics.
The first method is the BSM_NOA, a fixed thresholding method using a set of specifically designed and combined image enhancements, whilst the second one is the BSM_ITF, a decision tree classification approach based on a wide range of biophysical parameters.
Decision tree classification models were used to develop heuristics to select the suitable fuel cell design and operational conditions to improve the maximum power density while artificial neural network models (ANN) were developed to test the predictability of IV curves at the conditions where experimental results were not available.
