Topic
Decision tree model
About: Decision tree model is a research topic. Over the lifetime, 2256 publications have been published within this topic receiving 38142 citations.
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Papers
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02 Oct 2007TL;DR: A novel approach for the automatic extraction of trees and the delineation of the tree crowns from remote sensing data, based on co-registered colour-infrared aerial imagery and a digital surface model, proves the feasibility of this approach.
Abstract: In this paper, we present a novel approach for the automatic extraction of trees and the delineation of the tree crowns from remote sensing data, and report and evaluate the results obtained with different test data sets The approach is scale-invariant and is based on co-registered colour-infrared aerial imagery and a digital surface model (DSM) Our primary assumption is that the coarse structure of the crown, if represented at the appropriate level in scale-space, can be approximated with the help of an ellipsoid The fine structure of the crown is suppressed at this scale level and can be ignored Our approach is based on a tree model with three geometric parameters (size, circularity and convexity of the tree crown) and one radiometric parameter for the tree vitality The processing strategy comprises three steps First, we segment a wide range of scale levels of a pre-processed version of the DSM In the second step, we select the best hypothesis for a crown from the overlapping segments of all levels based on the tree model The selection is achieved with the help of fuzzy functions for the tree model parameters Finally, the crown boundary is refined using active contour models (snakes) The approach was tested with four data sets from different sensors and exhibiting different resolutions The results are very promising and prove the feasibility of the new approach for automatic tree extraction from remote sensing data
77 citations
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09 Jul 2010TL;DR: A fast learning method for a graphical probabilistic model for discrete speech recognition based on spoken Arabic digit recognition by means of a new proposed spanning tree structure that takes advantage of the temporal nature of speech signal is introduced.
Abstract: This paper introduces a fast learning method for a graphical probabilistic model for discrete speech recognition based on spoken Arabic digit recognition by means of a new proposed spanning tree structure that takes advantage of the temporal nature of speech signal. The experimental results obtained on a spoken Arabic digit dataset confirmed that for the same rate of recognition the proposed method, in terms of time computation is much faster than the state of art algorithm that use the maximum weight spanning tree (MWST).
76 citations
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TL;DR: The case study of vehicle-leaving-roadway accident with ITA illustrates that the proposed methodology may not only capture the essential information transformations of accident that occur in system operation, but also determine the various combinations of hardware faults, software failures and human errors that could result in the occurrence of specified undesired incident at the system level even accident.
76 citations
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TL;DR: A novel graphical visualization provides a more scientifically meaningful characterization of the subgroups identified by decision trees, and advocates the use of the newer CTree technique due to its simplicity and ease of interpretation.
Abstract: In many studies, it is of interest to identify population subgroups that are relatively homogeneous with respect to an outcome. The nature of these subgroups can provide insight into effect mechanisms and suggest targets for tailored interventions. However, identifying relevant subgroups can be challenging with standard statistical methods. We review the literature on decision trees, a family of techniques for partitioning the population, on the basis of covariates, into distinct subgroups who share similar values of an outcome variable. We compare two decision tree methods, the popular Classification and Regression tree (CART) technique and the newer Conditional Inference tree (CTree) technique, assessing their performance in a simulation study and using data from the Box Lunch Study, a randomized controlled trial of a portion size intervention. Both CART and CTree identify homogeneous population subgroups and offer improved prediction accuracy relative to regression-based approaches when subgroups are truly present in the data. An important distinction between CART and CTree is that the latter uses a formal statistical hypothesis testing framework in building decision trees, which simplifies the process of identifying and interpreting the final tree model. We also introduce a novel way to visualize the subgroups defined by decision trees. Our novel graphical visualization provides a more scientifically meaningful characterization of the subgroups identified by decision trees. Decision trees are a useful tool for identifying homogeneous subgroups defined by combinations of individual characteristics. While all decision tree techniques generate subgroups, we advocate the use of the newer CTree technique due to its simplicity and ease of interpretation.
75 citations
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14 Jun 1988TL;DR: The separation of small complexity classes is considered and some downward closure results are derived which show that some intuitively arrive at results that were published previously are misleading.
Abstract: The separation of small complexity classes is considered. Some downward closure results are derived which show that some intuitively arrive at results that were published previously are misleading. This is done by giving uniform versions of simulations in the decision-tree model of concrete complexity. The results also show that sublinear-time computation has enough power to code interesting questions in polynomial-time complexity. >
75 citations