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Related papers: Hybrid Tree-based Models for Insurance Claims

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Tree-based ensemble methods, as Random Forests and Gradient Boosted Trees, have been successfully used for regression in many applications and research studies. Furthermore, these methods have been extended in order to deal with uncertainty…

Machine Learning · Computer Science 2018-11-20 Myriam Tami , Marianne Clausel , Emilie Devijver , Adrien Dulac , Eric Gaussier , Stefan Janaqi , Meriam Chebre

Hidden Markov Models (HMMs) are powerful tools for modeling sequential data, where the underlying states evolve in a stochastic manner and are only indirectly observable. Traditional HMM approaches are well-established for linear sequences,…

Machine Learning · Statistics 2024-06-05 Farzan Vafa , Sahand Hormoz

Sequential model-based optimization sequentially selects a candidate point by constructing a surrogate model with the history of evaluations, to solve a black-box optimization problem. Gaussian process (GP) regression is a popular choice as…

Machine Learning · Statistics 2022-02-23 Jungtaek Kim , Seungjin Choi

We introduce a novel digital twin framework for predictive maintenance of long-term physical systems. Using monitoring tire health as an application, we show how the digital twin framework can be used to enhance automotive safety and…

Machine Learning · Computer Science 2024-08-13 Vispi Karkaria , Jie Chen , Christopher Luey , Chase Siuta , Damien Lim , Robert Radulescu , Wei Chen

We propose new methods for Support Vector Machines (SVMs) using tree architecture for multi-class classi- fication. In each node of the tree, we select an appropriate binary classifier using entropy and generalization error estimation, then…

Machine Learning · Computer Science 2017-08-29 Pittipol Kantavat , Boonserm Kijsirikul , Patoomsiri Songsiri , Ken-ichi Fukui , Masayuki Numao

A common approach to the claims reserving problem is based on generalized linear models (GLM). Within this framework, the claims in different origin and development years are assumed to be independent variables. If this assumption is…

Applications · Statistics 2013-06-18 Šárka Hudecová , Michal Pešta

Generalized linear models (GLM) are link function based statistical models. Many supervised learning algorithms are extensions of GLMs and have link functions built into the algorithm to model different outcome distributions. There are two…

Methodology · Statistics 2019-05-02 Colleen M. Farrelly , Srikanth Namuduri , Uchenna Chukwu

We investigate at decision trees that incorporate both traditional queries based on one attribute and queries based on hypotheses about the values of all attributes. Such decision trees are similar to ones studied in exact learning, where…

Computational Complexity · Computer Science 2022-03-18 Mohammad Azad , Igor Chikalov , Shahid Hussain , Mikhail Moshkov , Beata Zielosko

Tree-based methods are popular machine learning techniques used in various fields. In this work, we review their foundations and a general framework the importance sampled learning ensemble (ISLE) that accelerates their fitting process.…

Machine Learning · Statistics 2022-05-02 Yinuo Zeng

This paper presents a new approach for trees-based regression, such as simple regression tree, random forest and gradient boosting, in settings involving correlated data. We show the problems that arise when implementing standard…

Methodology · Statistics 2021-08-09 Assaf Rabinowicz , Saharon Rosset

We develop a framework for applying treewidth-based dynamic programming on graphs with "hybrid structure", i.e., with parts that may not have small treewidth but instead possess other structural properties. Informally, this is achieved by…

Data Structures and Algorithms · Computer Science 2019-08-28 Eduard Eiben , Robert Ganian , Thekla Hamm , O-joung Kwon

This paper proposes a multi-step probabilistic forecasting framework using a single neural-network based model to generate simultaneous point and interval forecasts. Our approach ensures non-crossing prediction intervals (PIs) through a…

Machine Learning · Computer Science 2026-04-21 Worachit Amnuaypongsa , Yotsapat Suparanonrat , Pana Wanitchollakit , Jitkomut Songsiri

In order to be able to process the increasing amount of spatial data, efficient methods for their handling need to be developed. One major challenge for big spatial data is access. This can be achieved through space-filling curves, as they…

Data Structures and Algorithms · Computer Science 2019-04-26 Markus Wilhelm Jahn , Patrick Erik Bradley

This paper presents a new procedure for evaluating the goodness of fit of Generalized Linear Models (GLM) estimated with Roadway Departure (RwD) crash frequency data for the State of Hawaii on two-lane two-way (TLTW) state roads. The…

Applications · Statistics 2022-05-04 Mohammadreza Hashemi , Adrian Ricardo Archilla

Merge trees are fundamental structures in topological data analysis. Interleaving distance is a widely accepted metric for comparing merge trees, with applications in visualization and scientific computing. While a greedy algorithm exists…

Computational Geometry · Computer Science 2025-09-22 Elena Farahbakhsh Touli , Talha Bin Masood

When examining the relationship between an exposure and an outcome, there is often a time lag between exposure and the observed effect on the outcome. A common statistical approach for estimating the relationship between the outcome and…

Methodology · Statistics 2025-04-28 Seongwon Im , Ander Wilson , Daniel Mork

Machine learning in high-stakes domains, such as healthcare, faces two critical challenges: (1) generalizing to diverse data distributions given limited training data while (2) maintaining interpretability. To address these challenges, we…

Machine Learning · Computer Science 2023-07-11 Keyan Nasseri , Chandan Singh , James Duncan , Aaron Kornblith , Bin Yu

This paper is concerned with the reliable inference of optimal tree-approximations to the dependency structure of an unknown distribution generating data. The traditional approach to the problem measures the dependency strength between…

Machine Learning · Computer Science 2007-07-16 Marco Zaffalon , Marcus Hutter

We study how to utilize (possibly erroneous) predictions in a model for computing under uncertainty in which an algorithm can query unknown data. Our aim is to minimize the number of queries needed to solve the minimum spanning tree…

Data Structures and Algorithms · Computer Science 2022-07-01 Thomas Erlebach , Murilo Santos de Lima , Nicole Megow , Jens Schlöter

Battery performance datasets are typically non-normal and multicollinear. Extrapolating such datasets for model predictions needs attention to such characteristics. This study explores the impact of data normality in building machine…

Machine Learning · Computer Science 2021-11-05 Shovan Chowdhury , Yuxiao Lin , Boryann Liaw , Leslie Kerby
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