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Thomas Kneib – One of the best experts on this subject based on the ideXlab platform.

  • Predicting the occurrence of wildfires with binary structured Additive Regression models.
    Journal of Environmental Management, 2016
    Co-Authors: Laura Ríos-pena, Thomas Kneib, Carmen Cadarso-suárez, M.f Marey-pérez

    Abstract:

    Abstract Wildfires are one of the main environmental problems facing societies today, and in the case of Galicia (north-west Spain), they are the main cause of forest destruction. This paper used binary structured Additive Regression (STAR) for modelling the occurrence of wildfires in Galicia. Binary STAR models are a recent contribution to the classical logistic Regression and binary generalized Additive models. Their main advantage lies in their flexibility for modelling non-linear effects, while simultaneously incorporating spatial and temporal variables directly, thereby making it possible to reveal possible relationships among the variables considered. The results showed that the occurrence of wildfires depends on many covariates which display variable behaviour across space and time, and which largely determine the likelihood of ignition of a fire. The joint possibility of working on spatial scales with a resolution of 1 × 1 km cells and mapping predictions in a colour range makes STAR models a useful tool for plotting and predicting wildfire occurrence. Lastly, it will facilitate the development of fire behaviour models, which can be invaluable when it comes to drawing up fire-prevention and firefighting plans.

  • Structured Additive Regression Models: An R Interface to BayesX
    Journal of Statistical Software, 2015
    Co-Authors: Nikolaus Umlauf, Thomas Kneib, Stefan Lang, Daniel Adler, Achim Zeileis

    Abstract:

    Structured Additive Regression (STAR) models provide a flexible framework for modeling possible nonlinear effects of covariates: They contain the well established frameworks of generalized linear models and generalized Additive models as special cases but also allow a wider class of effects, e.g., for geographical or spatio-temporal data, allowing for specification of complex and realistic models. BayesX is standalone software package providing software for fitting general class of STAR models. Based on a comprehensive open-source Regression toolbox written in C++, BayesX uses Bayesian inference for estimating STAR models based on Markov chain Monte Carlo simulation techniques, a mixed model representation of STAR models, or stepwise Regression techniques combining penalized least squares estimation with model selection. BayesX not only covers models for responses from univariate exponential families, but also models from less-standard Regression situations such as models for multi-categorical responses with either ordered or unordered categories, continuous time survival data, or continuous time multi-state models. This paper presents a new fully interactive R interface to BayesX: the R package R2BayesX. With the new package, STAR models can be conveniently specified using R’s formula language (with some extended terms), fitted using the BayesX binary, represented in R with objects of suitable classes, and finally printed/summarized/plotted. This makes BayesX much more accessible to users familiar with R and adds extensive graphics capabilities for visualizing fitted STAR models. Furthermore, R2BayesX complements the already impressive capabilities for semiparametric Regression in R by a comprehensive toolbox comprising in particular more complex response types and alternative inferential procedures such as simulation-based Bayesian inference.

  • Multilevel structured Additive Regression
    Statistics and Computing, 2014
    Co-Authors: Stefan Lang, Nikolaus Umlauf, Peter Wechselberger, Kenneth Harttgen, Thomas Kneib

    Abstract:

    Models with structured Additive predictor provide a very broad and rich framework for complex Regression modeling. They can deal simultaneously with nonlinear covariate effects and time trends, unit- or cluster-specific heterogeneity, spatial heterogeneity and complex interactions between covariates of different type. In this paper, we propose a hierarchical or multilevel version of Regression models with structured Additive predictor where the Regression coefficients of a particular nonlinear term may obey another Regression model with structured Additive predictor. In that sense, the model is composed of a hierarchy of complex structured Additive Regression models. The proposed model may be regarded as an extended version of a multilevel model with nonlinear covariate terms in every level of the hierarchy. The model framework is also the basis for generalized random slope modeling based on multiplicative random effects. Inference is fully Bayesian and based on Markov chain Monte Carlo simulation techniques. We provide an in depth description of several highly efficient sampling schemes that allow to estimate complex models with several hierarchy levels and a large number of observations within a couple of minutes (often even seconds). We demonstrate the practicability of the approach in a complex application on childhood undernutrition with large sample size and three hierarchy levels.

Suku Nair – One of the best experts on this subject based on the ideXlab platform.

  • Hardening Email Security via Bayesian Additive Regression Trees
    , 2009
    Co-Authors: Saeed Abu-nimeh, Dario Nappa, Xinlei Wang, Suku Nair

    Abstract:

    The changeable structures and variability of email attacks render current email filtering solutions useless. Consequently, the need for new techniques to harden the protection of users’ security and privacy becomes a necessity. The variety of email attacks, namely spam, damages networks’ infrastructure and exposes users to new attack vectors daily. Spam is unsolicited email which targets users with different types of commercial messages or advertisements. Porn-related content that contains explicit material or commercials of exploited children is a major trend in these messages as well. The waste of network bandwidth due to the numerous number of spam messages sent and the requirement of complex hardware, software, network resources, and human power are other problems associated with these attacks. Recently, security researchers have noticed an increase in malicious content delivered by these messages, which arises security concerns due to their attack potential. More seriously, phishing attacks have been on the rise for the past couple of years. Phishing is the act of sending a forged e-mail to a recipient, falsely mimicking a legitimate establishment in an attempt to scam the recipient into divulging private information such as credit card numbers or bank account passwords (James, 2005). Recently phishing attacks have become a major concern to financial institutions and law enforcement due to the heavy monetary losses involved. According to a survey by Gartner group, in 2006 approximately 3.25 million victims were spoofed by phishing attacks and in 2007 the number increased by almost 1.3 million victims. Furthermore, in 2007, monetary losses, related to phishing attacks, were estimated by $3.2 billion. All the aforementioned concerns raise the need for new detection mechanisms to subvert email attacks in their various forms. Despite the abundance of applications available for phishing detection, unlike spam classification, there are only few studies that compare machine learning techniques in predicting phishing emails (Abu-Nimeh et al., 2007). We describe a new version of Bayesian Additive Regression Trees (BART) and apply it to phishing detection. A phishing dataset is constructed from 1409 raw phishing emails and 5152 legitimate emails, where 71 features (variables) are used in classifiers’ training and testing. The variables consist of both textual and structural features that are extracted from raw emails. The performance of six classifiers, on this dataset, is compared using the area under the curve (AUC) (Huang & Ling, 2005). The classifiers include Logistic Regression (LR), Classification and Regression Trees (CART), Bayesian Additive Regression Trees (BART), Support Vector Machines (SVM), Random O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg

  • ICC – Distributed Phishing Detection by Applying Variable Selection Using Bayesian Additive Regression Trees
    2009 IEEE International Conference on Communications, 2009
    Co-Authors: Saeed Abu-nimeh, Dario Nappa, Xinlei Wang, Suku Nair

    Abstract:

    Phishing continue to be one of the most drastic attacks causing both financial institutions and customers huge monetary losses. Nowadays mobile devices are widely used to access the Internet and therefore access financial and confidential data. However, unlike PCs and wired devices, such devices lack basic defensive applications to protect against various types of attacks. In consequence, phishing has evolved to target mobile users in Vishing and SMishing attacks recently. This study presents a client-server distributed architecture to detect phishing e-mails by taking advantage of automatic variable selection in Bayesian Additive Regression Trees (BART). When combined with other classifiers, BART improves their predictive accuracy. Further the overall architecture proves to leverage well in resource constrained environments.

  • bayesian Additive Regression trees based spam detection for enhanced email privacy
    Availability Reliability and Security, 2008
    Co-Authors: Saeed Abunimeh, Xinlei Wang, Dario Nappa, Suku Nair

    Abstract:

    Spam is considered an invasion of privacy. Its changeable structures and variability raise the need for new spam classification techniques. The present study proposes using Bayesian Additive Regression trees (BART) for spam classification and evaluates its performance against other classification methods, including logistic Regression, support vector machines, classification and Regression trees, neural networks, random forests, and naive Bayes. BART in its original form is not designed for such problems, hence we modify BART and make it applicable to classification problems. We evaluate the classifiers using three spam datasets; Ling-Spam, PU1, and Spambase to determine the predictive accuracy and the false positive rate.

Rodney Sparapani – One of the best experts on this subject based on the ideXlab platform.

  • Fully Nonparametric Bayesian Additive Regression Trees
    Topics in Identification Limited Dependent Variables Partial Observability Experimentation and Flexible Modeling: Part B, 2019
    Co-Authors: Edward I. George, Robert E. Mcculloch, Purushottam W. Laud, Brent R. Logan, Rodney Sparapani

    Abstract:

    Bayesian Additive Regression trees (BART) is a fully Bayesian approach to modeling with ensembles of trees. BART can uncover complex Regression functions with high-dimensional regressors in a fairly automatic way and provide Bayesian quantification of the uncertainty through the posterior. However, BART assumes independent and identical distributed (i.i.d) normal errors. This strong parametric assumption can lead to misleading inference and uncertainty quantification. In this chapter we use the classic Dirichlet process mixture (DPM) mechanism to nonparametrically model the error distribution. A key strength of BART is that default prior settings work reasonably well in a variety of problems. The challenge in extending BART is to choose the parameters of the DPM so that the strengths of the standard BART approach is not lost when the errors are close to normal, but the DPM has the ability to adapt to non-normal errors.

  • Detection of Left Ventricular Hypertrophy Using Bayesian Additive Regression Trees: The MESA (Multi‐Ethnic Study of Atherosclerosis)
    Journal of the American Heart Association, 2019
    Co-Authors: Rodney Sparapani, Noura M. Dabbouseh, David D. Gutterman, Jun Zhang, Haiying Chen, David A. Bluemke, Joao A.c. Lima, Gregory L. Burke, Elsayed Z. Soliman

    Abstract:

    Background We developed a new left ventricular hypertrophy (LVH) criterion using a machine‐learning technique called Bayesian Additive Regression Trees (BART). Methods and Results This analysis inc…

  • detection of left ventricular hypertrophy using bayesian Additive Regression trees the mesa multi ethnic study of atherosclerosis
    Journal of the American Heart Association, 2019
    Co-Authors: Rodney Sparapani, Noura M. Dabbouseh, David D. Gutterman, Jun Zhang, Haiying Chen, David A. Bluemke, Joao A.c. Lima, Gregory L. Burke, Elsayed Z. Soliman

    Abstract:

    Background We developed a new left ventricular hypertrophy (LVH) criterion using a machine‐learning technique called Bayesian Additive Regression Trees (BART). Methods and Results This analysis inc…