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.

Knowledge discovery in data streams with regression tree methods

Abstract: This paper presents an advanced review of regression tree methods for mining data streams. Batch regression tree methods are known for their simplicity, interpretability, accuracy, and efficiency. They use fast divide-and-conquer greedy algorithms that recursively partition the given training data into smaller subsets. The result is a tree-shaped model with splitting rules in the internal nodes and predictions in the leaves. Most batch regression tree methods take a complete dataset and build a model using that data. Generally, this tree model cannot be modified if new data is acquired later. Their successors, the incremental model and interval trees algorithms, are able to build and retrain a model on a step-by-step basis by incorporating new numerical training instances into the model as they become available. Moreover, these algorithms produce even more compact and accurate models than batch regression tree algorithms because they use intervals or functional models with a change detection mechanism, which makes them a more suitable choice for regression analysis of data streams. Finally, this review summarizes the performance results of the reviewed methods and crystallizes 10 requirements for successful implementation of a regression tree algorithm in data stream mining area. © 2011 Wiley Periodicals, Inc.

Decision Model for Acute Appendicitis Treatment With Decision Tree Technology—A Modification of the Alvarado Scoring System

Abstract: Background How to decide the proper time to do laparotomies for acute appendicitis patients is sometimes very difficult, especially in areas with no imaging diagnostic tools. The Alvarado scoring system (ASS) is a convenient and inexpensive decision making tool; however, its accuracy needs to be improved. The decision tree is the most frequently used data mining technology for diagnostic model building. This study used a decision tree to modify the ASS and to prioritize the variables. Methods We collected 532 patients who underwent appendectomy. Patients who had undergone incidental appendectomy were excluded from the study. The decision tree algorithm was constructed with the data mining workbench Clementine version 8.1. It is a top-down algorithm designed to generate a decision tree model with entropy. The algorithm chooses the best decision node with which to separate different classes from empirical data. The Wilcoxon signed rank test, Student t test and χ 2 test were used for statistical analysis. Results Among the 532 patients recruited into the study, 420 had acute appendicitis and 112 had normal appendix. Women with acute appendicitis were older than their male counterparts ( p p Conclusion The new model is more convenient and accurate than ASS. Right lower quadrant tenderness is an inclusion criterion for acute appendicitis diagnosis. Migrating pain and neutrophil count > 75% were significant factors for acute appendicitis diagnosis if ASS score 10,000/dL were significantly different between acute appendicitis and normal appendix, there was no significant contribution of entropy change below the "neutrophil count > 75%" nodes in the model. So they were erased from the decision tree model. Further studies need to be conducted to investigate why older women are at higher risk for acute appendicitis.

Variable selection and sensitivity analysis using dynamic trees, with an application to computer code performance tuning

Abstract: We investigate an application in the automatic tuning of computer codes, an area of research that has come to prominence alongside the recent rise of distributed scientific processing and heterogeneity in high-performance computing environments. Here, the response function is nonlinear and noisy and may not be smooth or stationary. Clearly needed are variable selection, decomposition of influence, and analysis of main and secondary effects for both real-valued and binary inputs and outputs. Our contribution is a novel set of tools for variable selection and sensitivity analysis based on the recently proposed dynamic tree model. We argue that this approach is uniquely well suited to the demands of our motivating example. In illustrations on benchmark data sets, we show that the new techniques are faster and offer richer feature sets than do similar approaches in the static tree and computer experiment literature. We apply the methods in code-tuning optimization, examination of a cold-cache effect, and detection of transformation errors.

A simple tree swaying model for forest motion in windstorm conditions

Abstract: A simple tree swaying model, valid for windstorm conditions, has been developed for the purpose of simulating the effect of strong wind on the vulnerability of heterogeneous forest canopies. In this model the tree is represented as a flexible cantilever beam whose motion, induced by turbulent winds, is solved through a modal analysis. The geometric nonlinearities related to the tree curvature are accounted for through the formulation of the wind drag force. Furthermore, a breakage condition is considered at very large deflections. A variety of case studies is used to evaluate the present model. As compared to field data collected on three different tree species, and to the outputs of mechanistic models of wind damage, it appears to be able to predict accurately large tree deflections as well as tree breakage, using wind velocity at tree top as a forcing function. The instantaneous response of the modelled tree to a turbulent wind load shows very good agreement with a more complex tree model. The simplicity of the present model and its low computational time make it well adapted to future use in large-eddy simulation airflow models, aimed at simulating the complete interaction between turbulent wind fields and tree motion in fragmented forests.