Bayesian Additive Regression Trees using Bayesian model averaging
Sign Up to like & getrecommendations! Published in 2018 at "Statistics and Computing"
DOI: 10.1007/s11222-017-9767-1
Abstract: Bayesian Additive Regression Trees (BART) is a statistical sum of trees model. It can be considered a Bayesian version of machine learning tree ensemble methods where the individual trees are the base learners. However, for… read more here.
Keywords: regression trees; model; trees using; bart bma ... See more keywords
Data-driven switching modeling for MPC using Regression Trees and Random Forests
Sign Up to like & getrecommendations! Published in 2020 at "Nonlinear Analysis: Hybrid Systems"
DOI: 10.1016/j.nahs.2020.100882
Abstract: Abstract Model Predictive Control is a well consolidated technique to design optimal control strategies, leveraging the capability of a mathematical model to predict a system’s behavior over a time horizon. However, building physics-based models for… read more here.
Keywords: methodology; data driven; random forests; regression trees ... See more keywords
BARP: Improving Mister P Using Bayesian Additive Regression Trees
Sign Up to like & getrecommendations! Published in 2019 at "American Political Science Review"
DOI: 10.1017/s0003055419000480
Abstract: Multilevel regression and post-stratification (MRP) is the current gold standard for extrapolating opinion data from nationally representative surveys to smaller geographic units. However, innovations in nonparametric regularization methods can further improve the researcher’s ability to… read more here.
Keywords: regression; bayesian additive; regression trees; opinion data ... See more keywords
Log-Linear Bayesian Additive Regression Trees for Multinomial Logistic and Count Regression Models
Sign Up to like & getrecommendations! Published in 2017 at "Journal of the American Statistical Association"
DOI: 10.1080/01621459.2020.1813587
Abstract: Abstract We introduce Bayesian additive regression trees (BART) for log-linear models including multinomial logistic regression and count regression with zero-inflation and overdispersion. BART has been applied to nonparametric mean regression and binary classification problems in… read more here.
Keywords: regression; multinomial logistic; regression trees; log linear ... See more keywords
Bayesian Additive Regression Trees (BART) with covariate adjusted borrowing in subgroup analyses
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Biopharmaceutical Statistics"
DOI: 10.1080/10543406.2022.2089160
Abstract: ABSTRACT It is crucial in clinical trials to investigate treatment effect consistency across subgroups defined by patient baseline characteristics. However, there may be treatment effect variability across subgroups due to small subgroup sample size. Various… read more here.
Keywords: additive regression; trees bart; subgroup; bayesian additive ... See more keywords
Heteroscedastic BART via Multiplicative Regression Trees
Sign Up to like & getrecommendations! Published in 2019 at "Journal of Computational and Graphical Statistics"
DOI: 10.1080/10618600.2019.1677243
Abstract: Abstract Bayesian additive regression trees (BART) has become increasingly popular as a flexible and scalable nonparametric regression approach for modern applied statistics problems. For the practitioner dealing with large and complex nonlinear response surfaces, its… read more here.
Keywords: regression; regression trees; variance; bart via ... See more keywords
An Empirical Study on Predicting Blood Pressure Using Classification and Regression Trees
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Access"
DOI: 10.1109/access.2017.2787980
Abstract: Blood pressure (BP) diseases have become one of the major threats to human health. Continuous measurement of BP has proven to be a prerequisite for effective incident prevention. In contrast with the traditional prediction models… read more here.
Keywords: blood pressure; regression trees; classification regression; regression ... See more keywords
Distilled Neural Networks for Efficient Learning to Rank
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Knowledge and Data Engineering"
DOI: 10.1109/tkde.2022.3152585
Abstract: Recent studies in Learning to Rank have shown the possibility to effectively distill a neural network from an ensemble of regression trees. This result leads neural networks to become a natural competitor of tree-based ensembles… read more here.
Keywords: neural network; neural networks; scoring time; learning rank ... See more keywords
Aviation Turbulence Forecasting at Upper Levels with Machine Learning Techniques Based on Regression Trees
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Applied Meteorology and Climatology"
DOI: 10.1175/jamc-d-20-0116.1
Abstract: We explore the use of machine learning (ML) techniques, namely regression trees (RT), for the purpose of aviation turbulence forecasting at upper levels (20 − 45 kft altitude). In particular, we develop a series of… read more here.
Keywords: machine learning; turbulence; regression trees; model ... See more keywords
Motivation of Teleworkers and Non-teleworkers in Times of COVID-19 in Spain: An Exploratory Study Using Non-parametric Analysis and Classification and Regression Trees
Sign Up to like & getrecommendations! Published in 2022 at "Frontiers in Psychology"
DOI: 10.3389/fpsyg.2022.852758
Abstract: With the outbreak of COVID-19 in spring 2020, small, medium, and large companies were forced to cope with the unexpected circumstances. Faced by this health emergency, it was necessary to ensure that staff remained motivated… read more here.
Keywords: non parametric; using non; classification regression; regression trees ... See more keywords
Learning Cephalometric Landmarks for Diagnostic Features Using Regression Trees
Sign Up to like & getrecommendations! Published in 2022 at "Bioengineering"
DOI: 10.3390/bioengineering9110617
Abstract: Lateral cephalograms provide important information regarding dental, skeletal, and soft-tissue parameters that are critical for orthodontic diagnosis and treatment planning. Several machine learning methods have previously been used for the automated localization of diagnostically relevant… read more here.
Keywords: learning cephalometric; regression; diagnostic features; landmarks diagnostic ... See more keywords