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Related papers: Weighted Sum-of-Trees Model for Clustered Data

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Traditional machine learning approaches may fail to perform satisfactorily when dealing with complex data. In this context, the importance of data mining evolves w.r.t. building an efficient knowledge discovery and mining framework.…

Computer Vision and Pattern Recognition · Computer Science 2020-08-04 Abdul Mueed Hafiz , Ghulam Mohiuddin Bhat

Nowadays new technologies, and especially artificial intelligence, are more and more established in our society. Big data analysis and machine learning, two sub-fields of artificial intelligence, are at the core of many recent breakthroughs…

Machine Learning · Statistics 2021-06-22 Antonio Sutera

Composite endpoints are increasingly used in clinical trials to capture treatment effects across multiple or hierarchically ordered outcomes. Although inference procedures based on win statistics, such as the win ratio, win odds, and net…

Methodology · Statistics 2025-10-28 Xi Fang , Zhiqiang Cao , Fan Li

Random forests and, more generally, (decision\nobreakdash-)tree ensembles are widely used methods for classification and regression. Recent algorithmic advances allow to compute decision trees that are optimal for various measures such as…

Machine Learning · Computer Science 2024-09-25 Christian Komusiewicz , Pascal Kunz , Frank Sommer , Manuel Sorge

Introduction. We investigate the generalization ability of models built on datasets containing a small number of subjects, recorded in single study protocols. Next, we propose and evaluate methods combining these datasets into a single,…

Machine Learning · Computer Science 2023-12-05 Gideon Vos , Kelly Trinh , Zoltan Sarnyai , Mostafa Rahimi Azghadi

Data analysis and machine learning have become an integrative part of the modern scientific methodology, offering automated procedures for the prediction of a phenomenon based on past observations, unraveling underlying patterns in data and…

Machine Learning · Statistics 2015-06-04 Gilles Louppe

Eliciting preferences from human judgements is inherently imprecise, yet most decision analysis methods force a single priority vector from pairwise comparisons, discarding the information embedded in inconsistencies. We instead leverage…

General Economics · Economics 2026-02-27 Salvatore Greco , Sajid Siraj , Michele Lundy

Cluster-weighted models (CWMs) extend finite mixtures of regressions (FMRs) in order to allow the distribution of covariates to contribute to the clustering process. In a matrix-variate framework, the matrix-variate normal CWM has been…

We introduce a novel statistical significance-based approach for clustering hierarchical data using semi-parametric linear mixed-effects models designed for responses with laws in the exponential family (e.g., Poisson and Bernoulli). Within…

Methodology · Statistics 2025-02-04 Alessandra Ragni , Chiara Masci , Francesca Ieva , Anna Maria Paganoni

Inferring individualised treatment effects from observational data can unlock the potential for targeted interventions. It is, however, hard to infer these effects from observational data. One major problem that can arise is covariate shift…

Machine Learning · Computer Science 2024-04-25 Damian Machlanski , Spyros Samothrakis , Paul Clarke

Deep networks and decision forests (such as random forests and gradient boosted trees) are the leading machine learning methods for structured and tabular data, respectively. Many papers have empirically compared large numbers of…

Various modifications of decision trees have been extensively used during the past years due to their high efficiency and interpretability. Tree node splitting based on relevant feature selection is a key step of decision tree learning, at…

Machine Learning · Computer Science 2017-09-05 Dmitry Ignatov , Andrey Ignatov

This paper explores interpretability techniques for two of the most successful learning algorithms in medical decision-making literature: deep neural networks and random forests. We applied these algorithms in a real-world medical dataset…

Machine Learning · Computer Science 2020-02-24 Catarina Moreira , Renuka Sindhgatta , Chun Ouyang , Peter Bruza , Andreas Wichert

Regression trees and their ensemble methods are popular methods for nonparametric regression: they combine strong predictive performance with interpretable estimators. To improve their utility for locally smooth response surfaces, we study…

Methodology · Statistics 2021-09-13 Sören R. Künzel , Theo F. Saarinen , Edward W. Liu , Jasjeet S. Sekhon

This paper proposes FREEtree, a tree-based method for high dimensional longitudinal data with correlated features. Popular machine learning approaches, like Random Forests, commonly used for variable selection do not perform well when there…

Machine Learning · Statistics 2020-06-18 Yuancheng Xu , Athanasse Zafirov , R. Michael Alvarez , Dan Kojis , Min Tan , Christina M. Ramirez

Data clustering has received a lot of attention and numerous methods, algorithms and software packages are available. Among these techniques, parametric finite-mixture models play a central role due to their interesting mathematical…

Computer Vision and Pattern Recognition · Computer Science 2017-01-31 Israel D. Gebru , Xavier Alameda-Pineda , Florence Forbes , Radu Horaud

This article introduces a flexible and adaptive nonparametric method for estimating the association between multiple covariates and power spectra of multiple time series. The proposed approach uses a Bayesian sum of trees model to capture…

Methodology · Statistics 2021-10-01 Yakun Wang , Zeda Li , Scott A. Bruce

Interpretability has become incredibly important as machine learning is increasingly used to inform consequential decisions. We propose to construct global explanations of complex, blackbox models in the form of a decision tree…

Machine Learning · Computer Science 2019-01-28 Osbert Bastani , Carolyn Kim , Hamsa Bastani

Multilevel models (mixed-effect models or hierarchical linear models) are now a standard approach to analysing clustered and longitudinal data in the social, behavioural and medical sciences. This review article focuses on multilevel linear…

Methodology · Statistics 2019-07-16 George Leckie

Decision trees and random forest remain highly competitive for classification on medium-sized, standard datasets due to their robustness, minimal preprocessing requirements, and interpretability. However, a single tree suffers from high…

Machine Learning · Statistics 2025-12-02 Cencheng Shen , Yuexiao Dong , Carey E. Priebe
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