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.

An Adaptation of the Ethnographic Decision Tree Modeling Methodology for Developing Evidence-Driven Agent-Based Models

Abstract: This paper introduces the integration of the Ethnographic Decision Tree Modelling methodology into an evidence-driven lifecycle for developing agent-based social simulations. The manuscript also highlights the development advantages of using an Ethnographic Decision Tree Model to promote accountable validation and detailed justification of how agent-based models are built. The result from this methodology is a hierarchical, tree-like structure that represents the branching and possible outcomes of the decision-making process, which can then be implemented in an agent-based model. The original methodology grounds the representation of decision-making solely on ethnographic data, yet the discussed adaptation hereby furthers that by allowing the use of survey data. As a result, the final model is a composite based on a richly descriptive dataset containing observations and reported behaviour of individuals engaged in the same activity and context. This in turn is demonstrated to serve as a useful guide for the implementation of behaviour in an social simulations and also serve as a baseline for testing.

Virtual Tree Model Based on Environment Influence and Its Application

Abstract: With the development of virtual reality technology, simulation of plant growth is a hotspot in virtual reality area. In order to reflect the characteristic features of a particular kind of tree and the influence of environment during the process of simulating tree, this paper proposed an virtual tree model based ondynamic forest growing simulation system environment influence (VTMEI). In addition, this model is deployed to the dynamic forest growing simulation system and different tree species have been simulated to validate the virtual tree model. The application shows that the simulation result can remain more scientific and the dynamic forest growing simulation system can satisfy the need of real-time walkthrough in the virtual forest.

Identifiability of a Coalescent-based Population Tree Model

Abstract: Identifiability of evolutionary tree models has been a recent topic of discussion and some models have been shown to be non-identifiable. A coalescent-based rooted population tree model, originally proposed by Nielsen et al. 1998 [2], has been used by many authors in the last few years and is a simple tool to accurately model the changes in allele frequencies in the tree. However, the identifiability of this model has never been proven. Here we prove this model to be identifiable by showing that the model parameters can be expressed as functions of the probability distributions of subsamples. This a step toward proving the consistency of the maximum likelihood estimator of the population tree based on this model.

Tree Modeling Based on Two Orthogonal Images

Abstract: This paper introduces a method that is based on two orthogonal images, which only need the front and side images of the tree to create a 3D tree model that is similar to the real model from any angle. First, we get the front and side images of the tree, then combined with user interaction to extract the front and side silhouette of the tree, and extract the main trunk. Second, we establish a visual hull according to the silhouette of tree. Third, system uses a visual hull and the built-in subbranches to generate three-dimensional branch structure of tree. Finally, we add the leaves to the branch, and then we get a complete three-dimensional tree model. In this paper, we implement a system to demonstrate out method.