scholarly journals C&R Tree based Air Target Classification Using Kinematics

2014 ◽  
pp. 79-85
Author(s):  
Manish Garg ◽  
Upasna Singh
Keyword(s):  
Regression Tree ◽  
Classification And Regression Tree ◽  
Significant Feature ◽  
Target Classification ◽  
Target Class ◽  
Material Technology ◽  
Data Set ◽  
Counter Measures ◽  
Feature Vectors ◽  
Stealth Technology

Since the improvement in Anti Radar Material technology and stealth technology grows, there are immense counter measures that have opened to deny such technologies for classification to the adversary. At the same time it is observed that radar is continuously tracking the air target. This track data represents the kinematics which can be efficiently manipulated for effective classification without being deceived. The present study uses decision tree based classifier, specifically Classification and Regression Tree (CRT) algorithm over certain significant feature vectors. It classifies the data set of an air target into a target class where feature vectors are derived from the Radar Track Data using Matlab code. The work presented here aims to assess the performance of CRT. Although the methods and results presented here are for Air Target Classification, they may give insight for other applications.

Use of data mining techniques to classify length of stay of emergency department patients

2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Görkem Sariyer ◽  
Ceren Öcal Taşar ◽  
Gizem Ersoy Cepe
Keyword(s):  
Length Of Stay ◽  
Regression Tree ◽  
Secondary Data ◽  
Classification And Regression Tree ◽  
Urban Hospital ◽  
Data Set ◽  
Random Forest Classification ◽  
Forest Classification ◽  
High Level ◽  
Sensitivity Specificity

Abstract Emergency departments (EDs) are the largest departments of hospitals which encounter high variety of cases as well as high level of patient volumes. Thus, an efficient classification of those patients at the time of their registration is very important for the operations planning and management. Using secondary data from the ED of an urban hospital, we examine the significance of factors while classifying patients according to their length of stay. Random Forest, Classification and Regression Tree, Logistic Regression (LR), and Multilayer Perceptron (MLP) were adopted in the data set of July 2016, and these algorithms were tested in data set of August 2016. Besides adopting and testing the algorithms on the whole data set, patients in these sets were grouped into 21 based on the similarities in their diagnoses and the algorithms were also performed in these subgroups. Performances of the classifiers were evaluated based on the sensitivity, specificity, and accuracy. It was observed that sensitivity, specificity, and accuracy values of the classifiers were similar, where LR and MLP had somehow higher values. In addition, the average performance of the classifying patients within the subgroups outperformed the classifying based on the whole data set for each of the classifiers.

scholarly journals Modeling and Testing Landslide Hazard Using Decision Tree

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Mutasem Sh. Alkhasawneh ◽  
Umi Kalthum Ngah ◽  
Lea Tien Tay ◽  
Nor Ashidi Mat Isa ◽  
Mohammad Subhi Al-Batah
Keyword(s):  
Decision Tree ◽  
Surface Area ◽  
Slope Angle ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Slope Aspect ◽  
Tree Model ◽  
Texture Surface ◽  
Data Set ◽  
Exhaustive Chaid

This paper proposes a decision tree model for specifying the importance of 21 factors causing the landslides in a wide area of Penang Island, Malaysia. These factors are vegetation cover, distance from the fault line, slope angle, cross curvature, slope aspect, distance from road, geology, diagonal length, longitude curvature, rugosity, plan curvature, elevation, rain perception, soil texture, surface area, distance from drainage, roughness, land cover, general curvature, tangent curvature, and profile curvature. Decision tree models are used for prediction, classification, and factors importance and are usually represented by an easy to interpret tree like structure. Four models were created using Chi-square Automatic Interaction Detector (CHAID), Exhaustive CHAID, Classification and Regression Tree (CRT), and Quick-Unbiased-Efficient Statistical Tree (QUEST). Twenty-one factors were extracted using digital elevation models (DEMs) and then used as input variables for the models. A data set of 137570 samples was selected for each variable in the analysis, where 68786 samples represent landslides and 68786 samples represent no landslides. 10-fold cross-validation was employed for testing the models. The highest accuracy was achieved using Exhaustive CHAID (82.0%) compared to CHAID (81.9%), CRT (75.6%), and QUEST (74.0%) model. Across the four models, five factors were identified as most important factors which are slope angle, distance from drainage, surface area, slope aspect, and cross curvature.

Comparison of CART and discriminant analysis of morphometric data in foraminiferal taxonomy

2006 ◽  
Vol 29 (1) ◽  
pp. 153-162
Author(s):  
Pratul Kumar Saraswati ◽  
Sanjeev V Sabnis
Keyword(s):  
Discriminant Analysis ◽  
Test Data ◽  
Parametric Method ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Misclassification Error ◽  
Data Sets ◽  
Morphometric Data ◽  
Validation Data ◽  
Data Set

Paleontologists use statistical methods for prediction and classification of taxa. Over the years, the statistical analyses of morphometric data are carried out under the assumption of multivariate normality. In an earlier study, three closely resembling species of a biostratigraphically important genus Nummulites were discriminated by multi-group discrimination. Two discriminant functions that used diameter and thickness of the tests and height and length of chambers in the final whorl accounted for nearly 100% discrimination. In this paper Classification and Regression Tree (CART), a non-parametric method, is used for classification and prediction of the same data set. In all 111 iterations of CART methodology are performed by splitting the data set of 55 observations into training, validation and test data sets in varying proportions. In the validation data sets 40% of the iterations are correctly classified and only one case of misclassification in 49% of the iterations is noted. As regards test data sets, nearly 70% contain no misclassification cases whereas in about 25% test data sets only one case of misclassification is found. The results suggest that the method is highly successful in assigning an individual to a particular species. The key variables on the basis of which tree models are built are combinations of thickness of the test (T), height of the chambers in the final whorl (HL) and diameter of the test (D). Both discriminant analysis and CART thus appear to be comparable in discriminating the three species. However, CART reduces the number of requisite variables without increasing the misclassification error. The method is very useful for professional geologists for quick identification of species.

Cutaneous complications in hematopoietic cell transplant recipients: Impact of biopsy on patient management.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 7036-7036
Author(s):  
Oana Valeria Paun ◽  
Tycel Jovelle Phillips ◽  
PingFu Fu ◽  
Robert Novoa ◽  
Kord Honda ◽  
...  
Keyword(s):  
Classification Tree ◽  
Regression Tree ◽  
Hematopoietic Cell ◽  
Classification And Regression Tree ◽  
Cell Transplant ◽  
Hematopoietic Cell Transplant ◽  
Data Set ◽  
Post Transplant ◽  
Skin Biopsies ◽  
The Impact

7036 Background: Although skin biopsies are recommended for diagnostic purposes in hematopoietic cell transplant (HCT) recipients, their utility in directing management of post transplant cutaneous eruptions remains uncertain. Little evidence was found in support of this procedure either from a diagnostic or prognostic perspective. Methods: We retrospectively evaluated 351 consecutive HCT recipients transplanted at our institution between January 2005 and December 2011; 156 patients underwent 388 cutaneous biopsies. Results: The group that underwent cutaneous biopsy after transplantation and the group that was spared the procedure were homogenous with regards to age and gender. The pre-biopsy diagnosis and final diagnosis differed in 213 episodes (55%) as determined by histologic evaluation. Biopsy results led to a change in therapy in 61 of 388 (16%) biopsied rashes. With regards to therapy changes, 24 of 61 (39%) occurred in response to a clinical diagnosis of GVHD. In this situation the most frequently noted change was augmentation or addition of systemic immuno-suppression (19 of 24). Changes in systemic therapy occurred with similar frequencies with respect to concordance or discordance between clinical and histopathologic diagnosis (p = 0.12). We used classification and regression tree analysis to develop an algorithm to predict the biopsy yield as expressed by change of management. This is a non-parametric decision tree learning technique that produces a classification tree based on a categorical dependent variable, formed by a collection of rules based on variables in the modeling data set. Conclusions: Cutaneous biopsy findings often changed the clinical dermatologic diagnoses of HCT recipients; however, the impact of biopsy results on treatment decisions was less profound; altered diagnoses in patients who underwent biopsy often did not lead to therapy changes. Skin biopsies of post-transplant patients may not be mandatory for either diagnostic or therapeutic reasons, but in carefully chosen circumstances can yield extremely important data. A prospective study should be undertaken in order to evaluate current practice data and to validate our decision making analysis tree.

scholarly journals New Model for Predicting the Presence of Coronary Artery Calcification

2021 ◽  
Vol 10 (3) ◽  
pp. 457
Author(s):  
Samel Park ◽  
Min Hong ◽  
HwaMin Lee ◽  
Nam-jun Cho ◽  
Eun-Young Lee ◽  
...  
Keyword(s):  
Coronary Artery ◽  
Coronary Artery Calcification ◽  
Prediction Models ◽  
Regression Tree ◽  
Conditional Inference ◽  
Classification And Regression Tree ◽  
Health Study ◽  
Tree Model ◽  
Data Set ◽  
Patients Âgés

Coronary artery calcification (CAC) is a feature of coronary atherosclerosis and a well-known risk factor for cardiovascular disease (CVD). As the absence of CAC is associated with a lower incidence rate of CVD, measurement of a CAC score is helpful for risk stratification when the risk decision is uncertain. This was a retrospective study with an aim to build a model to predict the presence of CAC (i.e., CAC score = 0 or not) and evaluate the discrimination and calibration power of the model. Our data set was divided into two set (80% for training set and 20% for test set). Ten-fold cross-validation was applied with ten times of interaction in each fold. We built prediction models using logistic regression (LRM), classification and regression tree (CART), conditional inference tree (CIT), and random forest (RF). A total of 3302 patients from two cohorts (Soonchunhyang University Cheonan Hospital and Kangbuk Samsung Health Study) were enrolled. These patients’ ages were between 40 and 75 years. All models showed acceptable accuracies (LRM, 70.71%; CART, 71.32%; CIT, 71.32%; and RF, 71.02%). The decision tree model using CART and CIT showed a reasonable accuracy without complexity. It could be implemented in real-world practice.

scholarly journals Representative Classification of Teacher’s Perceptions of Externalizing Behaviors

2020 ◽  
Author(s):  
Jessica Bagneris
Keyword(s):  
African American ◽  
Problem Behaviors ◽  
Externalizing Behaviors ◽  
Regression Tree ◽  
Internalizing Behaviors ◽  
Classification And Regression Tree ◽  
Male Students ◽  
Data Set ◽  
Cart Analysis

*This report was prepared as part of course requirements for SOW 6938 and has not been submitted for publication in a peer-reviewed journal. Background: This study aimed to identify what contributors influenced whether a teacher observed externalizing behaviors among fourth-grade children. Methods: Data was obtained from the Early Childhood Longitudinal Studies (ECLS) Program data set provided by the National Center for Education Statistics. A Classification and Regression Tree (CART) analysis was conducted to determine the relative importance of the varying factors that were attributed to externalizing behaviors. Results: The CART analysis revealed that teacher classification of children as exhibiting externalizing problem behaviors was influenced by internalizing problem behaviors, race, and gender. Exhibiting internalizing behaviors was the most significant contributor. Lower internalizing behaviors were classified as lower externalizing behaviors. Higher internalizing behaviors were further classified by gender, with female students being less likely attributed to high externalizing behaviors. Male students were further classified by race; Caucasian and Hispanic male students were classified with lower externalizing behaviors than African American students. Caucasian and Hispanic students were then classified by internalizing behaviors, with higher internalizing behaviors being classified toward higher externalizing behaviors. Conclusion: The findings are supported by the extant literature stating that African American males are more often classified as exhibiting externalizing behaviors. Future implications for research and practice will be discussed.

Levels of Safety on Interurban Roads

1996 ◽  
Vol 1553 (1) ◽  
pp. 95-102 ◽  
Author(s):  
Alfred S. Hakkert ◽  
Irit Hocherman ◽  
Abraham Mensah
Keyword(s):  
Regression Tree ◽  
Flow Speed ◽  
Parametric Models ◽  
Classification And Regression Tree ◽  
Masking Effect ◽  
Pictorial Representation ◽  
Data Set ◽  
Explanatory Variables ◽  
Cart Analysis ◽  
Binary Tree Structure

The levels of safety of the main interurban road network in Israel are studied, and 70 percent of the total interurban roads are covered. A macro approach to the relation between road safety and various road categories as reflected in the overall geometric and traffic operational features is presented and is limited to road links. The classification and regression tree (CART) analysis, a nonparametric method that generates a binary tree structure from the data showing the criterion (variables) for each split and giving a pictorial representation of the data, is used as a preliminary tool to illuminate the relation between the variables and road accidents as a measure of safety and to identify meaningful candidate variables for deeper analysis. CART results portray the importance of average daily traffic (ADT) and the need for stratification of ADT in accident modeling. It also indicates probable power function models and highlights some interactive variables and the effect of shoulder type among single carriageways. However, no interactive variables were revealed because of the total dominance and masking effect of ADT. Multivariate multiplicative Poisson regression models with log links were fitted to the data set using the generalized linear interactive modeling package. The results again emphasize the safer nature of freeways as compared with conventional single- and dual-carriageway roads and show that the relation between ADT and safety is curvilinear. For the fitted parametric models, the effects of ADT were found to be most important, as in the case of the nonparametric CART analysis. In the case of models for single carriageway roads, other explanatory variables (free flow speed, type of shoulders, and junction frequency) showed some significance.

Improved Detection of Prostate Cancer Using Classification and Regression Tree Analysis

2005 ◽  
Vol 23 (19) ◽  
pp. 4322-4329 ◽  
Author(s):  
Mark Garzotto ◽  
Tomasz M. Beer ◽  
R. Guy Hudson ◽  
Laura Peters ◽  
Yi-Ching Hsieh ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prostate Biopsy ◽  
Prostate Volume ◽  
Specific Antigen ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Validation Data ◽  
Data Set ◽  
Cart Analysis ◽  
Classification And Regression

Purpose To build a decision tree for patients suspected of having prostate cancer using classification and regression tree (CART) analysis. Patients and Methods Data were uniformly collected on 1,433 referred men with a serum prostate-specific antigen (PSA) levels of ≤ 10 ng/mL who underwent a prostate biopsy. Factors analyzed included demographic, laboratory, and ultrasound data (ie, hypoechoic lesions and PSA density [PSAD]). Twenty percent of the data was randomly selected and reserved for study validation. CART analysis was performed in two steps, initially using PSA and digital rectal examination (DRE) alone and subsequently using the remaining variables. Results CART analysis selected a PSA cutoff of more than 1.55 ng/mL for further work-up, regardless of DRE findings. CART then selected the following subgroups at risk for a positive biopsy: (1) PSAD more than 0.165 ng/mL/cc; (2) PSAD ≤ 0.165 ng/mL/cc and a hypoechoic lesion; (3) PSAD ≤ 0.165 ng/mL/cc, no hypoechoic lesions, age older than 55.5 years, and prostate volume ≤ 44.0 cc; and (4) PSAD ≤ 0.165 ng/mL/cc, no hypoechoic lesions, age older than 55.5 years, and 50.25 cc less than prostate volume ≤ 80.8 cc. In the validation data set, specificity and sensitivity were 31.3% and 96.6%, respectively. Cancers that were missed by the CART were Gleason score 6 or less in 93.4% of cases. Receiver operator characteristic curve analysis showed that CART and logistic regression models had similar accuracy (area under the curve = 0.74 v 0.72, respectively). Conclusion Application of CART analysis to the prostate biopsy decision results in a significant reduction in unnecessary biopsies while retaining a high degree of sensitivity when compared with the standard of performing a biopsy of all patients with an abnormal PSA or DRE.

In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning

2019 ◽  
Vol 21 (9) ◽  
pp. 662-669 ◽  
Author(s):  
Junnan Zhao ◽  
Lu Zhu ◽  
Weineng Zhou ◽  
Lingfeng Yin ◽  
Yuchen Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Regression Tree ◽  
Large Data ◽  
Thrombin Inhibitors ◽  
Coagulation Cascade ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Set ◽  
Descriptor Selection

Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors. Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors. Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.

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