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The use of multivariate adaptive regression splines (MARS) and classification and regression tree (CART) to estimate the live body weight at weaning age of the Sussex cattle breed remain poorly understood in South Africa. This study was conducted to examine the effect of linear body measurements on body weight at weaning using MARS and CART algorithms. The body weight and linear body measurements included sternum height, withers height, heart girth, hip height, body length, rump length and rump width were collected from 101 Sussex cattle (female = 57 and male = 44) at weaning. Goodness of fit criterions was used to select the best data mining algorithms. The results showed that MARS showed higher predictive performance in the criteria as compared to CART algorithm. The findings of the study suggest that MARS algorithm can be used to estimate the BW at weaning age in Sussex cattle breed. These findings might be helpful to cattle farmers in the selection criterions of breeding stock at weaning age. [ABSTRACT FROM AUTHOR]

Sexual harassment and its negative consequences continue to affect a large percentage of higher education students in the US. Previous research has identified a limited number of harassment risk factors, and has generally not examined them in combination. In this study, an expanded set of individual, relationship, and community-level risk factors were examined using hurdle models and classification and regression tree (CART) analyses to identify key risk factors for peer and faculty/staff sexual harassment. Secondary data analysis was conducted using data from a sample of 9,285 students from 18 two-year and four-year schools in Minnesota. CART analyses indicated that, for peer sexual harassment, being younger; consuming alcohol more than once a month; attending a four-year school; being transgender, genderqueer, self-identified, or a cisgender woman; and having experienced bullying were the most important risk factors for peer harassment on campus. For faculty/staff harassment, being gay, lesbian, bisexual, questioning, or having a self-identified sexual orientation was the most important risk factor. These and other risk factors were significant in the hurdle models. More research is needed to understand why these factors are associated with harassment. Limitations and implications for prevention programming at higher education institutions are discussed. [ABSTRACT FROM AUTHOR]

Purpose: To develop an algorithm for the diagnosis of Behçet's disease (BD) uveitis based on ocular findings. Methods: Following an initial survey among uveitis experts, we collected multi-center retrospective data on 211 patients with BD uveitis and 207 patients with other uveitides, and identified ocular findings with a high diagnostic odds ratio (DOR). Subsequently, we collected multi-center prospective data on 127 patients with BD uveitis and 322 controls and developed a diagnostic algorithm using Classification and Regression Tree (CART) analysis and expert opinion. Results: We identified 10 items with DOR >5. The items that provided the highest accuracy in CART analysis included superficial retinal infiltrate, signs of occlusive retinal vasculitis, and diffuse retinal capillary leakage as well as the absence of granulomatous anterior uveitis or choroiditis in patients with vitritis. Conclusion: This study provides a diagnostic tree for BD uveitis that needs to be validated in future studies. [ABSTRACT FROM AUTHOR]

Recursive partitioning, also known as decision trees and classification and regression trees (CART), is a machine learning procedure that has gained traction in the behavioral sciences because of its ability to search for nonlinear and interactive effects, and produce interpretable predictive models. The recursive partitioning algorithm is greedy—searching for the variable and the splitting value that maximizes outcome homogeneity. Thus, the algorithm can be overly sensitive to chance associations in the data, particularly in small samples. In an effort to limit chance associations, we propose and evaluate a reliability-based cost function for recursive partitioning. The reliability-based cost function increases the likelihood of selecting variables that are more reliable, which should have more consistent associations with the outcome of interest. Two reliability-based cost functions are proposed, evaluated through simulation, and compared to the CART algorithm. Results indicate that reliability-based cost functions can be beneficial, particularly with smaller samples and when more reliable variables are important to the prediction, but can overlook important associations between the outcome and lower reliability predictors. The use of these cost functions was illustrated using data on depression and suicidal ideation from the National Longitudinal Survey of Youth. [ABSTRACT FROM AUTHOR]

Thanks to nonparametric estimators coming from machine learning, microlevel reserving has become more and more popular for actuaries. Recent research focused on how to integrate the whole information one can have on claims to predict individual reserves, with varying success due to incomplete observations. Using the CART algorithm, we develop new results that allow us to deal with large reporting delays and partially observed explanatory variables. Statistically speaking, we extend CART to take into account truncation of the data and introduce plug-in estimators. Our applications are based on real-life insurance portfolios embedding Income Protection and Third-Party Liability guarantees. The full knowledge of the claim lifetime is shown to be crucial to predict the individual reserves efficiently. [ABSTRACT FROM AUTHOR]

Background: Cesarean delivery (CD) is the most frequently performed surgical procedure worldwide. Trial of labor after cesarean (TOLAC) is associated with an increase in perinatal complications related to uterine rupture. However, in general, vaginal birth after cesarean (VBAC) is considered safe and women have less morbidity than those who undergo an elective repeat CD.Objective: To develop an integrated model with the best performance criteria for predicting vaginal delivery success after CD.Study design: Retrospective observational study including 2367 women who underwent a TOLAC. A predictive model using classification and regression tree modeling was constructed to predict vaginal delivery using maternal demographic, medical history, and labor predictors.Results: Vaginal delivery was best predicted by spontaneous onset of labor, estimated fetal weight <3775 g, maternal body mass index <25, previous CD as an elective or for fetal distress reasons, and interdelivery interval <2290 days. The algorithm showed a sensitivity of 75%, a specificity of 53%, and the area under the curve was 0.69.Conclusions: The classification and regression tree algorithm can be used to develop a predictive model for the success of TOLAC. [ABSTRACT FROM AUTHOR]

The multilevel logistic regression model (M-logit) is the standard model for modeling multilevel data with binary outcomes. However, many assumptions and restrictions should be considered when applying this model for unbiased estimation. To overcome these limitations, we proposed a multilevel CART (M-CART) algorithm which combines the M-logit and single level CART (S-CART) within the framework of the expectation-maximization. Simulation results showed that the proposed M-CART provided substantial improvements on classification accuracy, sensitivity, and specific over the M-logit, S-CART, and single level logistic regression model when modeling multilevel data with binary outcomes. This benefit of using M-CART was consistently found across different conditions of sample size, intra-class correlation, and when relationships between predictors and outcomes were nonlinear and nonadditive. [ABSTRACT FROM AUTHOR]

This article proposes a novel paradigm for building regression trees and ensemble learning in survival analysis. Generalizations of the classification and regression trees (CART) and random forests (RF) algorithms for general loss functions, and in the latter case more general bootstrap procedures, are both introduced. These results, in combination with an extension of the theory of censoring unbiased transformations (CUTs) applicable to loss functions, underpin the development of two new classes of algorithms for constructing survival trees and survival forests: censoring unbiased regression trees and censoring unbiased regression ensembles. For a certain "doubly robust" CUT of squared error loss, we further show how these new algorithms can be implemented using existing software (e.g., CART, RF). Comparisons of these methods to existing ensemble procedures for predicting survival probabilities are provided in both simulated settings and through applications to four datasets. It is shown that these new methods either improve upon, or remain competitive with, existing implementations of random survival forests, conditional inference forests, and recursively imputed survival trees. [ABSTRACT FROM AUTHOR]

Drought monitoring, and its impact management planning, has been a challenge for decision makers mainly because of lack of reliable information and decision support tools. The main objective of the study was to develop a remote sensing-based vegetation condition drought-monitoring approach for pastoralist areas using multi-temporal and spatial resolution satellite, climate, and biophysical datasets. Twenty-four years of data (1983-2006) from 11 attributes were extracted and used for developing the prediction models. A classification and regression tree (CART) modelling technique was used to integrate and model the drought parameters. Using the CART models, drought predictio n maps were produced for 2016, and the model outputs agreed with what had been reported by government and humanitarian partners of Ethiopia. The methodology can be used for future drought monitoring and early warning for risk-based (rather than crisis-based) drought planning. Future research may improve both the spatial and administrative resolution of the model so that drought status can be determined at district levels, which will be useful for actual drought mitigation planning. [ABSTRACT FROM AUTHOR]

The models based on vehicle speed have been used to estimate fuel consumption and CO2 emissions. However, these models could not properly estimate the change in fuel consumption and CO2 emissions as the speed changes. As for the alternative method, people try to consider using acceleration instead of speed. Although acceleration has been seriously considered, determining critical aggressive acceleration value in relation to fuel consumption and CO2 emissions is difficult to find. In this study, evaluation models of fuel consumption were developed using instantaneous acceleration, and we defined the critical aggressive acceleration values for different states of the vehicle from the viewpoints of fuel consumption and emissions. We used a mid-sized Liquefied Petroleum Gas (LPG) passenger car and obtained instantaneous data from a digital tachograph installed in the car while it accelerates. We developed two fuel consumption models and found critical aggressive accelerations, respectively: a model of starting vehicle that measures range of speed required to overcome the inertia during acceleration from stop state, and the other model for the driving state. We used Classification and Regression Tree (CART) analysis to find the critical aggressive accelerations at which the increments of fuel consumption change abruptly. As a result, the critical aggressive accelerations causing abrupt change in the increments of fuel consumption were found to be 2.598 m/s2 for the starting of vehicles and 1.4705 m/s2 when driving them. We also found that the increments of fuel consumption can be explained through quadratic and exponential functions with instantaneous acceleration. [ABSTRACT FROM AUTHOR]

Nummuliteswith reticulate septal filaments stratigraphically span from the Bartonian to the Rupelian Stages. The size of the proloculus of the megalospheric forms of reticulate species helped recogniseN. fabianii–N. fichtelilineage in western Tethys. Unlike the species of this lineage,N. ptukhiani, described from Armenia, is characterised by an unusually large proloculus. A possibly second lineage of reticulate species comprisingN. ptukhianiis recently reported from Lutetian – Bartonian succession of Tanzania. The present study examines reticulate species from palaeogeographically adjacent Bartonian succession of Kutch. The statistical analysis of the biometric data suggests the presence of three distinct species, referred toNummulites ptukhiani,N.aff.hormoensisandN. acutus. The reticulation starts developing inN. acutusthat ranges from P13 to P14 in its type locality, Kutch. We infer thatNummulites ptukhianiandN.aff.hormoensispossibly evolved fromN. acutusin Zone P14. A binary tree model based on Classification and Regression Tree is proposed to statistically discriminate the three reticulate species. [ABSTRACT FROM AUTHOR]

Many variables that are analyzed by social scientists are nominal in nature. When missing data occur on these variables, optimal recovery of the analysis model's parameters is a challenging endeavor. One of the most popular methods to deal with missing nominal data is multiple imputation (MI). This study evaluated the capabilities of five MI methods that can be used to treat incomplete nominal variables: multiple imputation with chained equations (MICE) using polytomous regression as the elementary imputation method; MICE based on classification and regression trees (CART); MICE based on nested logistic regressions; the ranking procedure described by Allison (2002); and a joint modeling approach based on the general location model. We first motivate our inquiry with an applied example and then present the results of a Monte Carlo simulation study that compared the performance of the five imputation methods under conditions of varying sample size, percentage of missing data, and number of nominal response categories. We found that MICE with polytomous regression was the strongest performer while the Allison (2002) ranking procedure and MICE with CART performed poorly in most conditions. [ABSTRACT FROM PUBLISHER]

Global Positioning System (GPS) technologies have been increasingly considered as an alternative to traditional travel survey methods to collect activity-travel data. Algorithms applied to extract activity-travel patterns vary from informal ad-hoc decision rules to advanced machine learning methods and have different accuracy. This paper systematically compares the relative performance of different algorithms for the detection of transportation modes and activity episodes. In particular, naive Bayesian, Bayesian network, logistic regression, multilayer perceptron, support vector machine, decision table, and C4.5 algorithms are selected and compared for the same data according to their overall error rates and hit ratios. Results show that the Bayesian network has a better performance than the other algorithms in terms of the percentage correctly identified instances and Kappa values for both the training data and test data, in the sense that the Bayesian network is relatively efficient and generalizable in the context of GPS data imputation. [ABSTRACT FROM AUTHOR]

An inversion algorithm based on the Classification and Regression Tree (CART) has been developed to retrieve sea ice from Moderate Resolution Imaging Spectroradiometer (MODIS) images in the Bohai Sea where the sea water is characterized by a high concentration of suspended sediment in coastal areas. The inversion algorithm has been successfully applied to the sea-ice extraction from 2009 to 2012. The estimated sea ice is compared with previous studies and the comparison shows reasonable agreement. The model is further examined using sea-ice data from higher-spatial-resolution satellites, and the result indicates that the CART method is able to successfully retrieve sea ice in high sediment environments in the Bohai Sea. To comprehensively understand the working principles of the CART, a series of sensitivity studies to model input parameters such as sampling locations, the number of bands, and the effect of the thermal infrared band (TIB), was conducted. The sensitivity studies show that the CART method is easy to set up and the results are realistic. The TIB may play an important role in sea-ice inversion in turbid waters. The algorithm is also compared with a ratio-threshold segmentation (RTS) method, a common way to retrieve sea ice from satellite images in open oceans, and the comparison indicates that the algorithm developed in the present article is superior to the RTS method in high sediment environments. [ABSTRACT FROM PUBLISHER]

The article presents the research on classification and regression trees algorithms.

Cell phone usage may impair a driver's decision-making at a dilemma zone. This research seeks to identify the impact of cell phone usage on different dilemma zone driver behaviors. Participants were exposed to different driving situations in a simulator where they had a phone call while driving through signalized intersections. A combination of variables was collected; therefore, this research estimates Classification and Regression Tree (CART) and stepwise logistic regression models to describe the factors influencing dilemma zone driver behavior. While the logistic regression models focus on the overall impact of the variables, the CART model develops subpopulations based on the variables' impact. Cell phone usage, especially incoming calls on a handheld device, and the overall experiment appear to encourage conservative behavior where the drivers opt to stop even when they are "expected" to go. Unfortunately, when the drivers decide to go, they tend to make the wrong choice and run the red light. Using hands-free devices on outgoing calls appears to reduce the likelihood of performing an illegal maneuver. This represents a potential opportunity for future policy and technological advancements to improve intersection safety by only permitting outgoing hands-free calls on arterials. [ABSTRACT FROM AUTHOR]