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Journal articles on the topic 'Tree-based Models'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Bigler, Christof, and Harald Bugmann. "Growth-dependent tree mortality models based on tree rings." Canadian Journal of Forest Research 33, no. 2 (2003): 210–21. http://dx.doi.org/10.1139/x02-180.

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Mortality is a crucial element of population dynamics. However, tree mortality is not well understood, particularly at the individual level. The objectives of this study were to (i) determine growth patterns (growth levels and growth trends) over different time windows that can be used to discriminate between dead and living Norway spruce (Picea abies (L.) Karst.) trees, (ii) optimize the selection of growth variables in logistic mortality models, and (iii) assess the impact of competition on recent growth in linear regression models. The logistic mortality model that we developed for mature s

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2

Shannon, William D., Maciej Faifer, Michael A. Province, and D. C. Rao. "Tree-Based Models for Fiting Stratified Linear Regression Models." Journal of Classification 19, no. 1 (2002): 113–30. http://dx.doi.org/10.1007/s00357-001-0035-9.

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3

Montgomery, Jacob M., and Santiago Olivella. "Tree-Based Models for Political Science Data." American Journal of Political Science 62, no. 3 (2018): 729–44. http://dx.doi.org/10.1111/ajps.12361.

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4

El-Rayes, Nesreen, Ming Fang, Michael Smith, and Stephen M. Taylor. "Predicting employee attrition using tree-based models." International Journal of Organizational Analysis 28, no. 6 (2020): 1273–91. http://dx.doi.org/10.1108/ijoa-10-2019-1903.

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Purpose The purpose of this study is to develop tree-based binary classification models to predict the likelihood of employee attrition based on firm cultural and management attributes. Design/methodology/approach A data set of resumes anonymously submitted through Glassdoor’s online portal is used in tandem with public company review information to fit decision tree, random forest and gradient boosted tree models to predict the probability of an employee leaving a firm during a job transition. Findings Random forest and decision tree methods are found to be the strongest attrition prediction

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5

Lee, Paul H., and Philip L. H. Yu. "Distance-based tree models for ranking data." Computational Statistics & Data Analysis 54, no. 6 (2010): 1672–82. http://dx.doi.org/10.1016/j.csda.2010.01.027.

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6

Zhai, Feifei, Jiajun Zhang, Yu Zhou, and Chengqing Zong. "Unsupervised Tree Induction for Tree-based Translation." Transactions of the Association for Computational Linguistics 1 (December 2013): 243–54. http://dx.doi.org/10.1162/tacl_a_00224.

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In current research, most tree-based translation models are built directly from parse trees. In this study, we go in another direction and build a translation model with an unsupervised tree structure derived from a novel non-parametric Bayesian model. In the model, we utilize synchronous tree substitution grammars (STSG) to capture the bilingual mapping between language pairs. To train the model efficiently, we develop a Gibbs sampler with three novel Gibbs operators. The sampler is capable of exploring the infinite space of tree structures by performing local changes on the tree nodes. Exper

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7

Marcelino, Concepcion Collado Jr, and Malawit Tumibay Gilbert. "Forecasting onion armyworm using tree-based machine learning models." Global Journal of Engineering and Technology Advances 16, no. 1 (2023): 001–7. https://doi.org/10.5281/zenodo.8275745.

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In the Philippines, the province of Nueva Ecija produces fifty-four percent of its annual onion production. However, the level of onion growth production was reduced; since the outbreak of 2016, armyworms destroyed thousands of hectares of farms resulting in a loss of billions of pesos, which lead to the decline of the onion harvest. In this study, we develop machine learning models to forecast an outbreak of armyworms to help evade or reduce the damage caused by an armyworm outbreak. Climatic data; particularly Maximum temperature, Minimum Temperature, Ultraviolet Index, Humidity, Cloudiness,

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8

Qin, Xiao, and Junhee Han. "Variable Selection Issues in Tree-Based Regression Models." Transportation Research Record: Journal of the Transportation Research Board 2061, no. 1 (2008): 30–38. http://dx.doi.org/10.3141/2061-04.

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Recently, there has been increasing interest in the use of classification and regression tree (CART) analysis. A tree-based regression model can be constructed by recursively partitioning the data with such criteria as to yield the maximum reduction in the variability of the response. Unfortunately, the exhaustive search may yield a bias in variable selection, and it tends to choose a categorical variable as a splitter that has many distinct values. In this study, an unbiased tree-based regression generalized unbiased interaction detection and estimation (GUIDE) model is introduced for its rob

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9

Park, Sangho, and Chanmin Kim. "Comparison of tree-based ensemble models for regression." Communications for Statistical Applications and Methods 29, no. 5 (2022): 561–89. http://dx.doi.org/10.29220/csam.2022.29.5.561.

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10

Tian, Y., J. L. Zhou, H. Lin, and H. Jiang. "Tree-Based Covariance Modeling of Hidden Markov Models." IEEE Transactions on Audio, Speech and Language Processing 14, no. 6 (2006): 2134–46. http://dx.doi.org/10.1109/tsa.2005.863210.

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11

Asadi, Nima, Jimmy Lin, and Arjen P. de Vries. "Runtime Optimizations for Tree-Based Machine Learning Models." IEEE Transactions on Knowledge and Data Engineering 26, no. 9 (2014): 2281–92. http://dx.doi.org/10.1109/tkde.2013.73.

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12

Desper, Richard, Feng Jiang, Olli-P. Kallioniemi, Holger Moch, Christos H. Papadimitriou, and Alejandro A. Schäffer. "Distance-Based Reconstruction of Tree Models for Oncogenesis." Journal of Computational Biology 7, no. 6 (2000): 789–803. http://dx.doi.org/10.1089/10665270050514936.

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13

Simonoff, Jeffrey S. "Regression tree-based diagnostics for linear multilevel models." Statistical Modelling: An International Journal 13, no. 5-6 (2013): 459–80. http://dx.doi.org/10.1177/1471082x13494612.

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14

Aldous, David. "Tree-based models for random distribution of mass." Journal of Statistical Physics 73, no. 3-4 (1993): 625–41. http://dx.doi.org/10.1007/bf01054343.

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15

Malhotra, D. K., Robert Nydick, and Nathan Coates. "USING TREE-BASED MODELS TO PREDICT CREDIT RISK." International Journal of Business Intelligence and Systems Engineering 1, no. 1 (2022): 1. http://dx.doi.org/10.1504/ijbise.2022.10054394.

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16

Coates, Nathan, Robert Nydick, and D. K. Malhotra. "Using tree-based models to predict credit risk." International Journal of Business Intelligence and Systems Engineering 2, no. 1 (2024): 23–42. http://dx.doi.org/10.1504/ijbise.2024.139147.

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17

Ninomiya, Seishi, and Vu Nguyen-Cong. "Evaluation of Soybean Plant Shape Based on Tree-Based Models." Ikushugaku zasshi 48, no. 3 (1998): 251–55. http://dx.doi.org/10.1270/jsbbs1951.48.251.

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18

Wang, Zijun, and Keke Gai. "Decision Tree-Based Federated Learning: A Survey." Blockchains 2, no. 1 (2024): 40–60. http://dx.doi.org/10.3390/blockchains2010003.

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Federated learning (FL) has garnered significant attention as a novel machine learning technique that enables collaborative training among multiple parties without exposing raw local data. In comparison to traditional neural networks or linear models, decision tree models offer higher simplicity and interpretability. The integration of FL technology with decision tree models holds immense potential for performance enhancement and privacy improvement. One current challenge is to identify methods for training and prediction of decision tree models in the FL environment. This survey addresses thi

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19

Uthayasuriyan, Agash, Ugochukwu Ilozurike Duru, Angela Nwachukwu, Thangavelu Shunmugasundaram, and Jeyakumar Gurusamy. "FLOW PATTERN PREDICTION IN HORIZONTAL AND INCLINED PIPES USING TREE-BASED AUTOMATED MACHINE LEARNING." Rudarsko-geološko-naftni zbornik 39, no. 4 (2024): 153–66. http://dx.doi.org/10.17794/rgn.2024.4.12.

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In the oil and gas industry, understanding two-phase (gas-liquid) flow is pivotal, as it directly influences equipment design, quality control, and operational efficiency. Flow pattern determination is thus fundamental to industrial engineering and management. This study utilizes the Tree-based Pipeline Optimization Tool (TPOT), an Automated Machine Learning (AutoML) framework that employs genetic programming, in obtaining the best machine learning model for a provided dataset. This paper presents the design of flow pattern prediction models using the TPOT. The TPOT was applied to predict flow

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20

Marcelino Concepcion Collado Jr and Gilbert Malawit Tumibay. "Forecasting onion armyworm using tree-based machine learning models." Global Journal of Engineering and Technology Advances 15, no. 3 (2023): 001–7. http://dx.doi.org/10.30574/gjeta.2023.15.3.0095.

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In the Philippines, the province of Nueva Ecija produces fifty-four percent of its annual onion production. However, the level of onion growth production was reduced; since the outbreak of 2016, armyworms destroyed thousands of hectares of farms resulting in a loss of billions of pesos, which lead to the decline of the onion harvest. In this study, we develop machine learning models to forecast an outbreak of armyworms to help evade or reduce the damage caused by an armyworm outbreak. Climatic data; particularly Maximum temperature, Minimum Temperature, Ultraviolet Index, Humidity, Cloudiness,

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21

Xuan, Haiyan, Cunliu Yao, Hongjian Li, and Xiaoke Chang. "Portfolio Model Based on Scenario Tree." E3S Web of Conferences 251 (2021): 01114. http://dx.doi.org/10.1051/e3sconf/202125101114.

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The uncertainty of return rate will affect the investment decision. In this paper, the ARMA-GARCH model is used to describe the data characteristics of stock returns, and the Monte Carlo method is used to construct a scenario tree containing the stock return rate and node probability. The decision rules are used to determine the nodes on the scene tree, and two mean-variance models are established based on the scene tree. Finally, four stock data are selected to optimize the portfolio of the constructed model, the results show that the scenario tree has good advantages in describing the uncert

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22

Kim, Jaehwan, and Il-Kwon Jeong. "Single Image-Based 3D Tree and Growth Models Reconstruction." ETRI Journal 36, no. 3 (2014): 450–59. http://dx.doi.org/10.4218/etrij.14.0113.0069.

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23

Liu, Chanjuan, Shijie Zhou, You-Gan Wang, and Zhihua Hu. "Natural mortality estimation using tree-based ensemble learning models." ICES Journal of Marine Science 77, no. 4 (2020): 1414–26. http://dx.doi.org/10.1093/icesjms/fsaa058.

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Abstract Empirical studies are popular in estimating fish natural mortality rate (M). However, these empirical methods derive M from other life-history parameters and are often perceived as being less reliable than direct methods. To improve the predictive performance and reliability of empirical methods, we develop ensemble learning models, including bagging trees, random forests, and boosting trees, to predict M based on a dataset of 256 records of both Chondrichthyes and Osteichthyes. Three common life-history parameters are used as predictors: the maximum age and two growth parameters (gro

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24

Jin, Qiwei, David B. Thomas, Wayne Luk, and Benjamin Cope. "Exploring Reconfigurable Architectures for Tree-Based Option Pricing Models." ACM Transactions on Reconfigurable Technology and Systems 2, no. 4 (2009): 1–17. http://dx.doi.org/10.1145/1575779.1575781.

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25

Komboz, Basil, Carolin Strobl, and Achim Zeileis. "Tree-Based Global Model Tests for Polytomous Rasch Models." Educational and Psychological Measurement 78, no. 1 (2016): 128–66. http://dx.doi.org/10.1177/0013164416664394.

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Psychometric measurement models are only valid if measurement invariance holds between test takers of different groups. Global model tests, such as the well-established likelihood ratio (LR) test, are sensitive to violations of measurement invariance, such as differential item functioning and differential step functioning. However, these traditional approaches are only applicable when comparing previously specified reference and focal groups, such as males and females. Here, we propose a new framework for global model tests for polytomous Rasch models based on a model-based recursive partition

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26

Wu, Yuncheng, Shaofeng Cai, Xiaokui Xiao, Gang Chen, and Beng Chin Ooi. "Privacy preserving vertical federated learning for tree-based models." Proceedings of the VLDB Endowment 13, no. 12 (2020): 2090–103. http://dx.doi.org/10.14778/3407790.3407811.

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27

Zambrano, Jenny, Noelle G. Beckman, Philippe Marchand, et al. "The scale dependency of trait‐based tree neighborhood models." Journal of Vegetation Science 31, no. 4 (2020): 581–93. http://dx.doi.org/10.1111/jvs.12880.

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28

Lizzi, Matteo. "A Contrast-Tree-Based Approach to Two-Population Models." Risks 12, no. 10 (2024): 152. http://dx.doi.org/10.3390/risks12100152.

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Building small-population mortality tables has great practical importance in actuarial applications. In recent years, several works in the literature have explored different methodologies to quantify and assess longevity and mortality risk, especially within the context of small populations, and many models dealing with this problem usually use a two-population approach, modeling a mortality spread between a larger reference population and the population of interest, via likelihood-based techniques. To broaden the tools at actuaries’ disposal to build small-population mortality tables, a gener

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29

Allman, E. S., S. Petrović, J. A. Rhodes, and S. Sullivant. "Identifiability of Two-Tree Mixtures for Group-Based Models." IEEE/ACM Transactions on Computational Biology and Bioinformatics 8, no. 3 (2011): 710–22. http://dx.doi.org/10.1109/tcbb.2010.79.

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30

Aldous, D., and B. Larget. "A tree-based scaling exponent for random cluster models." Journal of Physics A: Mathematical and General 25, no. 17 (1992): L1065—L1069. http://dx.doi.org/10.1088/0305-4470/25/17/009.

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31

Wunder, Jan, Christof Bigler, Björn Reineking, Lorenz Fahse, and Harald Bugmann. "Optimisation of tree mortality models based on growth patterns." Ecological Modelling 197, no. 1-2 (2006): 196–206. http://dx.doi.org/10.1016/j.ecolmodel.2006.02.037.

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32

Del Moral, Pierre, Frédéric Patras, and Sylvain Rubenthaler. "Tree based functional expansions for Feynman–Kac particle models." Annals of Applied Probability 19, no. 2 (2009): 778–825. http://dx.doi.org/10.1214/08-aap565.

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33

Haslinger, Robert, Gordon Pipa, Laura D. Lewis, Danko Nikolić, Ziv Williams, and Emery Brown. "Encoding Through Patterns: Regression Tree–Based Neuronal Population Models." Neural Computation 25, no. 8 (2013): 1953–93. http://dx.doi.org/10.1162/neco_a_00464.

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Although the existence of correlated spiking between neurons in a population is well known, the role such correlations play in encoding stimuli is not. We address this question by constructing pattern-based encoding models that describe how time-varying stimulus drive modulates the expression probabilities of population-wide spike patterns. The challenge is that large populations may express an astronomical number of unique patterns, and so fitting a unique encoding model for each individual pattern is not feasible. We avoid this combinatorial problem using a dimensionality-reduction approach

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34

Zhu, Min, David Philpotts, and Maxwell J. Stevenson. "The benefits of tree-based models for stock selection." Journal of Asset Management 13, no. 6 (2012): 437–48. http://dx.doi.org/10.1057/jam.2012.17.

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35

Kretowska, Malgorzata. "Tree-based models for survival data with competing risks." Computer Methods and Programs in Biomedicine 159 (June 2018): 185–98. http://dx.doi.org/10.1016/j.cmpb.2018.03.017.

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36

Palaparthi, Blessington Naveen, and S. Akthar. "Clickthrough Rate Prediction with Tree-based Machine Learning Models." International Journal of Innovative Research in Engineering and Management 12, no. 1 (2025): 28–33. https://doi.org/10.55524/ijirem.2025.12.1.4.

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This paper introduces an intuitive approach to clickthrough rate (CTR) prediction, a learning problem that has been extensively studied over the past several years. As digital marketing continues to grow rapidly into a multi-billion-dollar industry, this study aims to find the most effective machine learning model to enhance the CTR of marketing emails by comparing various tree-based models. Key steps in this research include data collection, feature extraction, and CTR prediction through the evaluation of different models. The statistical results prove that the CatBoost model, with optimized

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37

Rehman, Khawar, Yung-Chieh Wang, Muhammad Waseem, and Seung Ho Hong. "Tree-based machine learning models for prediction of bed elevation around bridge piers." Physics of Fluids 34, no. 8 (2022): 085105. http://dx.doi.org/10.1063/5.0098394.

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Scouring around bridge piers is a highly nonlinear process making its prediction by deterministic and stochastic models challenging. This study explores the application of inferential models for predictions of bed elevations around bridge piers. The objective is to get a generalized machine learning model with an interpretable structure. The historical data comprise a detailed record of streamflow and bed elevations that were captured by sensors installed at the 5th Street Bridge piers over Ocmulgee River at Macon, GA. We investigate the accuracy and efficiency of various tree-based machine le

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38

Fang, R. "IMPACTS OF TREE HEIGHT-DBH ALLOMETRY ON LIDAR-BASED TREE ABOVEGROUND BIOMASS MODELING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 23, 2016): 625–28. http://dx.doi.org/10.5194/isprs-archives-xli-b8-625-2016.

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Lidar has been widely used in tree aboveground biomass (AGB) estimation at plot or stand levels. Lidar-based AGB models are usually constructed with the ground AGB reference as the response variable and lidar canopy indices as predictor variables. Tree diameter at breast height (dbh) is the major variable of most allometric models for estimating reference AGB. However, lidar measurements are mainly related to tree vertical structure. Therefore, tree height-dbh allometric model residuals are expected to have a large impact on lidar-based AGB model performance. This study attempts to investigate

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39

Park, Young-Jin, and Frank F. Saccomanno. "Collision Frequency Analysis Using Tree-Based Stratification." Transportation Research Record: Journal of the Transportation Research Board 1908, no. 1 (2005): 121–29. http://dx.doi.org/10.1177/0361198105190800115.

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Although several factors are known to contribute to collisions at highway–railway grade crossings, the mixed effects of the control factors such as a highway class and other countermeasures on collision occurrence are less well explored. This study evaluates the relationship between countermeasures and collision occurrence by using a sequential analytic strategy that combines the tree-based data stratification method with the generalized linear regression technique. After the control factor effects were controlled with the use of the tree-based data partitioning method, stratified collision pr

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40

Fang, R. "IMPACTS OF TREE HEIGHT-DBH ALLOMETRY ON LIDAR-BASED TREE ABOVEGROUND BIOMASS MODELING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 23, 2016): 625–28. http://dx.doi.org/10.5194/isprsarchives-xli-b8-625-2016.

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Lidar has been widely used in tree aboveground biomass (AGB) estimation at plot or stand levels. Lidar-based AGB models are usually constructed with the ground AGB reference as the response variable and lidar canopy indices as predictor variables. Tree diameter at breast height (dbh) is the major variable of most allometric models for estimating reference AGB. However, lidar measurements are mainly related to tree vertical structure. Therefore, tree height-dbh allometric model residuals are expected to have a large impact on lidar-based AGB model performance. This study attempts to investigate

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41

Constable, J. V. H., and A. L. Friend. "Suitability of process-based tree growth models for addressing tree response to climate change." Environmental Pollution 110, no. 1 (2000): 47–59. http://dx.doi.org/10.1016/s0269-7491(99)00289-4.

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42

Zhao, Tianyi. "Survivals of Titanic Prediction Utilizing Tree-based Machine Learning Models." Highlights in Business, Economics and Management 40 (September 1, 2024): 284–88. http://dx.doi.org/10.54097/fwnnrc23.

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The shipwreck of Titanic is a well-known tragedy. Although it happened more than a century ago, researchers are still investigating the patterns of the survivors to gain more insight into human behaviors in catastrophes. This paper adopts machine learning techniques, including decision tree, random forest, and gradient boosting, to conduct a binary classification to predict whether a person survived. The selected models are all tree-based, making it convenient to examine the importance of features. In the preprocessing stage, all numerical features are discretized. This paper first investigate

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43

Wang, W. X., L. P. Hong, H. S. Huang, et al. "FILLING GAPS BETWEEN MESHES OF TREE CROWN AND TREE TRUNK BASED ON BOUNDARY CONSTRAINTS AND COORDINATE PROJECTION FOR 3D TREE MODELLING." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W2-2023 (December 13, 2023): 1081–87. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w2-2023-1081-2023.

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Abstract. In order to reconstruct high-quality 3D tree models, trunks and crowns could be reconstructed using appropriate methods separately and merged together. During this process, gaps will appear after tree models are spliced, which will affect the models' topological connectivity and visual effects. In this paper, a gap-repair algorithm for tree mesh models based on boundary restriction and coordinates projection is proposed. The algorithm first extracts all the holes in the tree trunk meshes and crown meshes and matched them according to their relative positions. Then, the gaps in the tr

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44

Guo, Wei Gang, Wei Han, and Shu Yan Liu. "Dynamic Fault Tree Based on Weibull Distribution." Advanced Materials Research 308-310 (August 2011): 1322–27. http://dx.doi.org/10.4028/www.scientific.net/amr.308-310.1322.

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The fault tree analysis is a widely used method for evaluation of systems reliability and safety. Dynamic fault tree (DFT) extend traditional FT by defining additional gates called dynamic gates to model these complex interactions. Markov models are used in solving dynamic gates. However, state space becomes too large for calculation with Markov models when the number of gate inputs increases. In addition, Markov model is applicable for only exponential failure and repair distributions. But in engineering, the failure mode is mostly obeyed to weibull distribution for mechanism and electronic u

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45

Pinheiro, H. S. K., P. R. Owens, L. H. C. Anjos, W. Carvalho Júnior, and C. S. Chagas. "Tree-based techniques to predict soil units." Soil Research 55, no. 8 (2017): 788. http://dx.doi.org/10.1071/sr16060.

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Quantitative soil–landscape models offer a method for conducting soil surveys that use statistical tools to predict natural patterns in the occurrence of particular map units across a landscape. The aim of the present study was to predict soil units in a watershed with wide variation in landscape conditions. The approach relied on a modelling of soil-forming factors in order to understand the variability of the landscape components in the region. Models were generated for landscape attributes related to pedogenesis, specifically elevation, slope, curvature, compound topographic index, Euclidea

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46

Kohut, Yurii, and Iryna Yurchak. "Recommendation System for Purchasing Goods Based on the Decision Tree Algorithm." Advances in Cyber-Physical Systems 6, no. 2 (2021): 121–27. http://dx.doi.org/10.23939/acps2021.02.121.

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Over the past few years, interest in applications related to recommendation systems has increased significantly. Many modern services create recommendation systems that, based on user profile information and his behavior. This services determine which objects or products may be interesting to users. Recommendation systems are a modern tool for understanding customer needs. The main methods of constructing recommendation systems are the content-based filtering method and the collaborative filtering method. This article presents the implementation of these methods based on decision trees. The co

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47

Newberry, James D., Harold E. Burkhart, and Ralph L. Amateis. "Individual tree merchantable volume to total volume ratios based on geometric solids." Canadian Journal of Forest Research 19, no. 5 (1989): 679–83. http://dx.doi.org/10.1139/x89-105.

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Individual tree volume ratio equations were developed on the basis of the volume formulas of certain geometric solids. Two parameter-free and two parameterized models were formulated, based on different basal diameters. The parameter-free models should be useful when few individual tree volume ratio data exist for a given species or for a particular population of trees. The parameterized models are appropriate, of course, to those situations where volume ratio data exist. The parameter-free models performed as well as the parameterized models in the upper half of the tree stem for the loblolly

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48

LI, Xiao-Bo. "Exploration of carcinogenesis based on tree models using CGH data." Hereditas (Beijing) 30, no. 4 (2008): 407–12. http://dx.doi.org/10.3724/sp.j.1005.2008.00407.

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49

Butler, Bret W., and Matthew B. Dickinson. "Tree Injury and Mortality in Fires: Developing Process-Based Models." Fire Ecology 6, no. 1 (2010): 55–79. http://dx.doi.org/10.4996/fireecology.0601055.

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50

Wundervald, Bruna, Andrew C. Parnell, and Katarina Domijan. "Generalizing Gain Penalization for Feature Selection in Tree-Based Models." IEEE Access 8 (2020): 190231–39. http://dx.doi.org/10.1109/access.2020.3032095.

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