This article develops a novel stochastic tree ensemble method for nonlinear regression, referred to as accelerated Bayesian additive regression trees, or XBART. By combining regularization and search strategies from modeling with computationally efficient techniques recursive partitioning algorithms, XBART attains state-of-the-art performance at prediction function estimation. Simulation studie...

Land cover mapping for large regions often employs satellite images of medium to coarse spatial resolution, which complicates mapping of discrete classes. Class memberships, which estimate the proportion of each class for every pixel, have been suggested as an alternative. This paper compares different strategies of training data allocation for discrete and continuous land cover mapping using c...

In this article, we propose a new behavioral modeling approach, called boosted model tree, to characterize and compensate for the complex nonlinear distortions induced by wideband high-efficiency radio frequency power amplifiers. With proposed model, input data are classified into different zones decision trees each zone is assigned separate submodels. We also employ boosting technique build mu...

MOTIVATION With complex traits and diseases having potential genetic contributions of thousands of genetic factors, and with current genotyping arrays consisting of millions of single nucleotide polymorphisms (SNPs), powerful high-dimensional statistical techniques are needed to comprehensively model the genetic variance. Machine learning techniques have many advantages including lack of parame...

A classification or regression tree is a prediction model that can be represented as a decision tree. This article discusses the C4.5, CART, CRUISE, GUIDE, and QUEST methods in terms of their algorithms, features, properties, and performance.

We present the ideas and methodologies that we used to address the KDD Cup 2009 challenge on rank-ordering the probability of churn, appetency and up-selling of wireless customers. We choose stochastic gradient boosting tree (TreeNet R ) as our main classifier to handle this large unbalanced dataset. In order to further improve the robustness and accuracy of our results, we bag a series of boos...