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Decision tree classification methodology [41] was used to construct a recursive decision tree designed to classify a sample to one of the four treatment groups based solely on its chromatogram.
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The proposed method involves the fusion of classifiers based on decision trees designed using fuzzy techniques.
Due to the decision tree design of the maze, the subjects had to plan their actions depending on the trap configuration up to two steps ahead to successfully retrieve the reward.
Study Design: A decision tree was designed that compared four possible screens for Down syndrome: (1) current second-trimester expanded maternal serum α-fetoprotein test (AFP), (2) first-trimester nuchal translucency screen, (3) first-trimester serum screen, and (4) combined first-trimester screen with both nuchal translucency screen and a serum screen.
The decision tree is designed to drive the inference engine to explore the route of selection procedure of modeling.
Largely based on the experiences of the presented SIA tools focusing land use policy advice, a decision tree is designed, which may facilitate an adequate, region and developmental phase specific selection of tool-box components for the development of SIA tools in future.
To analyze disease evolution with and without the vaccination programme, a decision tree was designed to include all possible events.
An existing decision tree model, designed using data from a large international clinical trial of palivizumab versus no prophylaxis, was updated to include Spanish observational hospitalisation data.
The handbook consists of a six-step decision framework and the use of decision trees specifically designed for three different environments: food production systems, inhabited areas and water environments.
Adaboost learning method is then applied on decision trees to design a classifier for fingerprint classification.
Most decision tree classifiers are designed to classify the data with categorical or Boolean class labels.
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