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.

The Factors of Subjective Voice Disorder Using Integrated Method of Decision Tree and Multi-Layer Perceptron Artificial Neural Network Algorithm

Abstract: The aim of the present study was to develop a prediction model for subjective voice disorders based on an artificial neural network algorithm and a decision tree using national statistical data. Subjects of analysis were 8,713 adults over the age of 19 (3,801 males and 4,912 females) who completed the otolaryngological examination of the Korea National Health and Nutrition Examination Survey from 2010 to 2012. Explanatory variables included age, education level, income, occupation, problem drinking, coffee consumption, and pain and discomfort from disease over the last two weeks. A multi-layer perceptron artificial neural network and a decision tree model were used for the analysis. In this model, smoking, pain and discomfort from disease over the last two weeks, education level, occupation, and income were drawn out as major predictors of subjective voice disorders. In order to minimize the risk of dysphonia, it is necessary to establish a scientific management system for high-risk groups.

The Establishment of Decision Tree Model in Network Traffic Incident Using C4.5 Method

Abstract: Computer network traffic is computer activity information in a network which explains interconnection between communication processes. The complexity of network traffic itself depends on the number of communication models used. The utilization of classification analyzing of network incident traffic is one of utilization forms of network traffic. The accuracy of classification model itself depends heavily on the formation of data training. The usage of C4.5 method in the establishment of decision tree in data training toward network incident traffic is with the basis to see the efficiency of decision tree formation. Wireshark tool is used as network capture tool to perform data acquisition in the formation of data training.

Computer models for computation and verification of bronchial morphology

Abstract: Accurate physiological measurements of the parameters like branching angles, branch lengths, and diameters of bronchial tree structures help in addressing the mechanistic and diagnostic questions related to obstructive lung disease. In order to facilitate these measurements, bronchial trees are reduced to a central axis tree. The approach we take employs first setting up a theoretical computerized tree structure, and then applying a 3D analysis to obtain the required anatomical data. A stick model was set up in 3D, with segment endpoints and diameters as input parameters to the model generator. By fixing the direction in which the slices are taken, a stack of 2D images of the generated 3D tree model is obtained, thereby simulating bronchial data sets. We design a two pass algorithm to compute the central axis tree and apply it on our models. In the first pass, the topological tree T is obtained by implementing a top-down seeded region growing algorithm of the 3D tree model. In the second pass, T is used to region growth along the axes of the branches. As the 3D tree model is traversed bottom-up, the centroid values of the cross sections of the branches are stored in the corresponding branch of T. At each bifurcation, the branch point and the three direction vectors along the branches are computed, by formulating it as a nonlinear optimization problem that minimizes the sum of least squares error of the centroid points of the corresponding branches. By connecting the branch points with straight lines, we obtain a reconstructed central axis tree which closely corresponds to the input stick model. We also studied the effect of adding external noise to out tree models and evaluating the physiological parameters. We conclude with the results of our algorithm on real airway trees.

Tree lightweight 3D reconstruction method based on enhanced PyrLK optical flow method

Abstract: The invention discloses a tree lightweight 3D reconstruction method based on an enhanced PyrLK optical flow method Firstly, for solving problems that a traditional pyramid LK optical flow method does not support rotation of characteristic points and does not have bidirectional coupling performance, enhancement of support affine transformation and reverse direction tracking on the PyrLK optical flow method is carried out, and an instance is given to explain robustness after enhancement; secondly, a complete tree framework is extracted through a three dimensional voxel flooding and linearity fitting method; lightweight at a designated degree on the tree framework is carried out based on combination of vertical and transverse limbs to meet Web lightweight application requirements; then an objective tree modeling reduction degree evaluation method is further provided; lastly, a model improvement method based on user interaction is further provided for acquiring a tree model which can completely meet user demands Through the method, the 3D tree model having properties of better lightweight and higher accuracy can be acquired

Efficient algorithms for selection of recovery points in tree task models

Abstract: Efficient solutions to the problem of optimally selecting recovery points are developed. The solutions are intended for models of computation in which task precedence has a tree structure and a task may fail due to the presence of faults. An algorithm to minimize the expected computation time of the task system under a uniprocessor environment has been developed for the binary tree model. The algorithm has time complexity of O(N/sub 2/), where N is the number of tasks, while previously reported procedures have exponential time requirements. The results are generalized for an arbitrary tree model. >