Random Forests and Adaptive Nearest Neighbors

Random Forests

Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538

Greedy function approximation: A gradient boosting machine

Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731

The Elements of Statistical Learning

Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970

Bagging predictors

Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325

Experiments with a new boosting algorithm

Freund, Y., & Schapire, R. E. (1996) · Cited by 8,189

Multivariate Adaptive Regression Splines

Friedman, J. H. (1991) · The Annals of Statistics · doi:10.1214/aos/1176347963 · Cited by 8,108

The random subspace method for constructing decision forests

Ho, T. K. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.709601 · Cited by 7,005

Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors)

Friedman, J., Hastie, T., & Tibshirani, R. (2000) · The Annals of Statistics · doi:10.1214/aos/1016218223 · Cited by 6,884

Pattern Recognition and Neural Networks

Ripley, B. D. (1996) · Cambridge University Press eBooks · doi:10.1017/cbo9780511812651 · Cited by 6,500

The Strength of Weak Learnability

Schapire, R. E. (1990) · Machine Learning · doi:10.1007/bf00116037 · Cited by 4,600

Computational prediction of inter-species relationships through omics data analysis and machine learning

Leite, D. M. C., Brochet, X., Resch, G., Que, Y., Neves, A., & Peña-Reyes, C. A. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2388-7 · Cited by 66

Journal of Business Cycle Measurement and Analysis

Gérard Biau, A., Biau, O., & Laurent Rouvière, C. (2024) · Journal of Business Cycle Measurement and Analysis · doi:10.1787/17293626 · Cited by 66

Calibrating random forests for probability estimation

Dankowski, T., & Ziegler, A. (2016) · Statistics in Medicine · doi:10.1002/sim.6959 · Cited by 65

Novel 3D Structure Based Model for Activity Prediction and Design of Antimicrobial Peptides

Liu, S., Bao, J., Lao, X., & Zheng, H. (2018) · Scientific Reports · doi:10.1038/s41598-018-29566-5 · Cited by 64

Probabilistic forecasting of crop yields via quantile random forest and Epanechnikov Kernel function

Gyamerah, S. A., Ngare, P. O., & Ikpe, D. C. (2019) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2019.107808 · Cited by 63

Random forests for metric learning with implicit pairwise position dependence

Xiong, C., Johnson, D., Xu, R., & Corso, J. J. (2012) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2339530.2339680 · Cited by 62

Gene Selection Using Iterative Feature Elimination Random Forests for Survival Outcomes

Pang, H., George, S. L., Hui, K. Y., & Tong, T. (2012) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2012.63 · Cited by 65

Prediction of delayed retention of antibodies in hydrophobic interaction chromatography from sequence using machine learning

Jain, T., Boland, T., Lilov, A., Burnina, I., Brown, M., Xu, Y., & Vásquez, M. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx519 · Cited by 61

Towards convergence rate analysis of random forests for classification

Gao, W. G., Xu, F., & Zhou, Z.-H. (2022) · Artificial Intelligence · doi:10.1016/j.artint.2022.103788 · Cited by 62

Ensemble Learning in Hyperspectral Image Classification: Toward Selecting a Favorable Bias-Variance Tradeoff

Merentitis, A., Debes, C., & Heremans, R. (2014) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2013.2295513 · Cited by 61

Consistency of Online Random ForestsPreprint

Denil, M., Matheson, D., & de Freitas, N. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1302.4853 · Cited by 59

Researcher reasoning meets computational capacity: Machine learning for social science

Lundberg, I., Brand, J. E., & Jeon, N. (2022) · Social Science Research · doi:10.1016/j.ssresearch.2022.102807 · Cited by 63

Advancements in hydrogen production through the integration of renewable energy sources with AI techniques: A comprehensive literature review

Baseer, M. A., Kumar, P., & Nascimento, E. G. S. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.125354 · Cited by 57

Comparison of the Performance of Machine Learning Models in Representing High-Dimensional Free Energy Surfaces and Generating Observables

Cendagorta, J. R., Tolpin, J., Schneider, E., Topper, R. Q., & Tuckerman, M. E. (2020) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.0c01218 · Cited by 56

Modeling of time series using random forests: Theoretical developments

Davis, R. A., & Nielsen, M. S. (2020) · Electronic Journal of Statistics · doi:10.1214/20-ejs1758 · Cited by 56

Streamflow Hydrology Estimate Using Machine Learning ( SHEM )

Petty, T. R., & Dhingra, P. (2017) · JAWRA Journal of the American Water Resources Association · doi:10.1111/1752-1688.12555 · Cited by 55

A predictive performance comparison of machine learning models for judicial cases

Liu, Z., & Chen, H. (2017) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2017.8285436 · Cited by 59

Predictive Feature Generation and Selection Using Process Data From PISA Interactive Problem-Solving Items: An Application of Random Forests

Han, Z., He, Q., & von Davier, M. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.02461 · Cited by 58

A generic framework for geotechnical subsurface modeling with machine learning

Xie, J., Huang, J., Zeng, C., Huang, S., & Burton, G. J. (2022) · Journal of Rock Mechanics and Geotechnical Engineering · doi:10.1016/j.jrmge.2022.08.001 · Cited by 53

Intervention in prediction measure: a new approach to assessing variable importance for random forests

Epifanio, I. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1650-8 · Cited by 53

Assessing the roles of temperature, carbon inputs and airborne pollen as drivers of fructification in European temperate deciduous forests

Lebourgeois, F., Delpierre, N., Dufrêne, É., Cecchini, S., Macé, S., Croisé, L., & Nicolas, M. (2018) · European Journal of Forest Research · doi:10.1007/s10342-018-1108-1 · Cited by 53

Data assimilation to reduce uncertainty of crop model prediction with Convolution Particle Filtering

Chen, Y., & Cournède, P.-H. (2014) · Ecological Modelling · doi:10.1016/j.ecolmodel.2014.01.030 · Cited by 53

Predicting the Tensile Behaviour of Cast Alloys by a Pattern Recognition Analysis on Experimental Data

Fragassa, C., Babič, M., Bergmann, C. P., & Minak, G. (2019) · Metals · doi:10.3390/met9050557 · Cited by 53

Oblique Decision Tree Ensemble via Twin Bounded SVM

Ganaie, M. A., Tanveer, M., & Suganthan, P. N. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.113072 · Cited by 52

Half a century experience in rate of penetration management: Application of machine learning methods and optimization algorithms - A review

Najjarpour, M., Jalalifar, H., & Norouzi-Apourvari, S. (2021) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2021.109575 · Cited by 52

Analysis of purely random forests biasPreprint

Arlot, S., & Genuer, R. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1407.3939 · Cited by 51

The macroeconomy as a random forest

Coulombe, P. G. (2024) · Journal of Applied Econometrics · doi:10.1002/jae.3030 · Cited by 50

Foundations and Innovations in Data Fusion and Ensemble Learning for Effective Consensus

Du, K.-L., Zhang, R., Jiang, B., Zeng, J., & Lu, J. (2025) · Mathematics · doi:10.3390/math13040587 · Cited by 55

Randomization as Regularization: A Degrees of Freedom Explanation for Random Forest SuccessPreprint

Mentch, L., & Zhou, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.00190 · Cited by 55

Tree Space Prototypes

Tan, S., Soloviev, M., Hooker, G., & Wells, M. T. (2020) · ACM-IMS Foundations of Data Science Conference (FODS) · doi:10.1145/3412815.3416893 · Cited by 54

Using random forests to estimate win probability before each play of an NFL game

Lock, D., & Nettleton, D. (2014) · Journal of Quantitative Analysis in Sports · doi:10.1515/jqas-2013-0100 · Cited by 34

Hepatic and serum lipid signatures specific to nonalcoholic steatohepatitis in murine models

Chiappini, F., Desterke, C., Bertrand‐Michel, J., Guettier, C., & Naour, F. L. (2016) · Scientific Reports · doi:10.1038/srep31587 · Cited by 48

Retooling Poverty Targeting Using Out-of-Sample Validation and Machine Learning

McBride, L., & Nichols, A. (2016) · The World Bank Economic Review · doi:10.1093/wber/lhw056 · Cited by 51

When do random forests fail?

Tang, C., Garreau, D., & Ulrike Von Luxburg, A. (2019) · MPG.PuRe (Max Planck Society) · Cited by 47

Asymptotic Theory for Random ForestsPreprint

Wager, S. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1405.0352 · Cited by 47

ECG Based Biometric Identification for Population with Normal and Cardiac Anomalies Using Hybrid HRV and DWT Features

Dar, M. N., Akram, M. U., Shaukat, A., & Khan, M. A. (2015) · International Conference on IT Convergence and Security (ICITCS) · doi:10.1109/icitcs.2015.7292977 · Cited by 45

Global estimates of 500 m daily aerodynamic roughness length from MODIS data

Peng, Z., Tang, R., Jiang, Y., Liu, M., & Li, Z.-L. (2021) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2021.11.015 · Cited by 46

Fitting Prediction Rule Ensembles with R Package pre

Fokkema, M. (2020) · Journal of Statistical Software · doi:10.18637/jss.v092.i12 · Cited by 44

Consistent estimation of residual variance with random forest Out-Of-Bag errors

Ramosaj, B., & Pauly, M. (2019) · Statistics & Probability Letters · doi:10.1016/j.spl.2019.03.017 · Cited by 44

Ground motion amplification models for Japan using machine learning techniques

Kim, S., Hwang, Y., Seo, H., & Kim, B. (2020) · Soil Dynamics and Earthquake Engineering · doi:10.1016/j.soildyn.2020.106095 · Cited by 44

Synthetic learning machines

Ishwaran, H., & Malley, J. D. (2014) · BioData Mining · doi:10.1186/s13040-014-0028-y · Cited by 43

Prediction intervals with random forests

Roy, M.-H., & Larocque, D. (2019) · Statistical Methods in Medical Research · doi:10.1177/0962280219829885 · Cited by 43

A systematic approach in constructing typical meteorological year weather files using machine learning

Hosseini, M., Bigtashi, A., & Lee, B. (2020) · Energy and Buildings · doi:10.1016/j.enbuild.2020.110375 · Cited by 43

Tree Space Prototypes: Another Look at Making Tree Ensembles InterpretablePreprint

Tan, S., Soloviev, M., Hooker, G., & Wells, M. T. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.07115 · Cited by 47

Comparing Pointwise and Listwise Objective Functions for Random-Forest-Based Learning-to-Rank

Ibrahim, M., & Carman, M. (2016) · ACM Transactions on Information Systems · doi:10.1145/2866571 · Cited by 41

Pathway analysis using random forests with bivariate node-split for survival outcomes

Pang, H., Datta, D., & Zhao, H. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp640 · Cited by 41

Separating the wheat from the chaff

Stijven, S., Minnebo, W., & Vladislavleva, K. (2011) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/2001858.2002059 · Cited by 41

Survival forests for data with dependent censoring

Moradian, H., Larocque, D., & Bellavance, F. (2017) · Statistical Methods in Medical Research · doi:10.1177/0962280217727314 · Cited by 40

Ligand- and Structure-Based Virtual Screening in Drug Discovery

Bhunia, S. S., Saxena, M., & Saxena, A. K. (2021) · Topics in medicinal chemistry · doi:10.1007/7355_2021_130 · Cited by 41

Machine learning-based predictive modeling to identify genotypic traits associated with Salmonella enterica disease endpoints in isolates from ground chicken

Tanui, C. K., Karanth, S., Njage, P. M. K., Meng, J., & Pradhan, A. K. (2021) · LWT · doi:10.1016/j.lwt.2021.112701 · Cited by 39

Rates of convergence for random forests via generalized U-statistics

Peng, W., Coleman, T., & Mentch, L. (2022) · Electronic Journal of Statistics · doi:10.1214/21-ejs1958 · Cited by 38

Asymptotic properties of high-dimensional random forests

Chi, C.-M., Vossler, P., Fan, Y., & Lv, J. (2022) · The Annals of Statistics · doi:10.1214/22-aos2234 · Cited by 36

Predictive Distribution Modeling Using Transformation Forests

Hothorn, T., & Zeileis, A. (2021) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2021.1872581 · Cited by 36

$$L_1$$ L 1 splitting rules in survival forests

Moradian, H., Larocque, D., & Bellavance, F. (2016) · Lifetime Data Analysis · doi:10.1007/s10985-016-9372-1 · Cited by 35

optRF: Optimising random forest stability by determining the optimal number of trees

Lange, T. M., Gültaş, M., Schmitt, A. O., & Heinrich, F. (2025) · BMC Bioinformatics · doi:10.1186/s12859-025-06097-1 · Cited by 34

Classifying flows and buffer state for youtube's HTTP adaptive streaming service in mobile networks

Tsilimantos, D., Karagkioules, T., & Valentin, S. (2018) · ACM Multimedia Systems Conference (MMSys) · doi:10.1145/3204949.3204955 · Cited by 34

The usefulness of socio-demographic variables in predicting purchase decisions: Evidence from machine learning procedures

Islam, T., Meade, N., Carson, R. T., Louviere, J. J., & Wang, J. (2022) · Journal of Business Research · doi:10.1016/j.jbusres.2022.07.004 · Cited by 38

Towards generating random forests via extremely randomized trees

Zhang, L., Ren, Y., & Suganthan, P. N. (2014) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2014.6889537 · Cited by 32

Can Alert Models for Fraud Protect the Elderly Clients of a Financial Institution?

Kumar, G., Muckley, C. B., Pham, L. H., & Ryan, D. (2018) · European Journal of Finance · doi:10.1080/1351847x.2018.1552603 · Cited by 32

Non-parametric individual treatment effect estimation for survival data with random forests

Tabib, S., & Larocque, D. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz602 · Cited by 32

Machine Learning Techniques applied in risk assessment related to food safety

IZSTO, UBESP, DSCB, ZETA, Ru, G., Crescio, M. I., Ingravalle, F., Maurella, C., Gregori, D., Lanera, C., et al. (2017) · EFSA Supporting Publications · doi:10.2903/sp.efsa.2017.en-1254 · Cited by 32

Tree aggregation for random forest class probability estimation

Sage, A. J., Genschel, U., & Nettleton, D. S. (2020) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11446 · Cited by 31

Estimating residual variance in random forest regression

Mendez, G., & Lohr, S. L. (2011) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2011.04.022 · Cited by 31

Modelling an Adaptive e-Learning System Using LSTM and Random Forest Classification

Anantharaman, H., Mubarak, A., & Shobana, B. T. (2018) · 2018 IEEE Conference on e-Learning, e-Management and e-Services (IC3e) · doi:10.1109/ic3e.2018.8632646 · Cited by 30

Estimating population average causal effects in the presence of non-overlap: The effect of natural gas compressor station exposure on cancer mortality

Nethery, R. C., Mealli, F., & Dominici, F. (2019) · The Annals of Applied Statistics · doi:10.1214/18-aoas1231 · Cited by 30

Boosting Hankel matrices for face emotion recognition and pain detection

Presti, L. L., & Cascia, M. L. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.10.007 · Cited by 30

Improvements to random forest methodology

Xu, R. (2013) · doi:10.31274/etd-180810-3436 · Cited by 29

Theory of Random Forests

Scornet, E., & Hooker, G. (2026) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-112723-034707 · Cited by 33

Trees, forests, chickens, and eggs: when and why to prune trees in a random forest

Zhou, S., & Mentch, L. (2022) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11594 · Cited by 30

Learning from evolving data streams through ensembles of random patches

Gomes, H. M., Read, J., Bifet, A., & Durrant, R. J. (2021) · Knowledge and Information Systems · doi:10.1007/s10115-021-01579-z · Cited by 28

Effects of gear characteristics on the presence of bigeye tuna (Thunnus obesus) in the catches of the purse-seine fishery of the eastern Pacific Ocean

Lennert‐Cody, C. E., Roberts, J. J., & Stephenson, R. J. (2008) · ICES Journal of Marine Science · doi:10.1093/icesjms/fsn075 · Cited by 29

Constrained Clustering With Imperfect Oracles

Zhu, X., Loy, C. C., & Gong, S. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2387425 · Cited by 28

Extremely randomized trees

(2010) · Automation and control engineering/Automation and control engineering series · doi:10.1201/9781439821091-a1 · Cited by 28

Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression

Ćevid, D., Michel, L., Näf, J., Bühlmann, P., & Meinshausen, N. (2020) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000585771 · Cited by 26

Optimal Causal Decision Trees Ensemble for Improved Prediction and Causal Inference

Younas, N., Ali, A., Hina, H., Hamraz, M., Khan, Z., & Aldahmani, S. (2022) · IEEE Access · doi:10.1109/access.2022.3146406 · Cited by 26

Assessing myocardial infarction severity from the urban environment perspective in Wuhan, China

Yao, Y., Yin, H., Xu, C., Chen, D., Shao, L., Guan, Q., & Wang, R. (2022) · Journal of Environmental Management · doi:10.1016/j.jenvman.2022.115438 · Cited by 26

Evolution of Random Forest from Decision Tree and Bagging: A Bias-Variance Perspective

Ibrahim, M. (2023) · Dhaka University Journal of Applied Science and Engineering · doi:10.3329/dujase.v7i1.62888 · Cited by 24

Consistency of survival tree and forest models: splitting bias and correction

Cui, Y., Zhu, R., Zhou, M., & Kosorok, M. R. (2020) · Statistica Sinica · doi:10.5705/ss.202020.0263 · Cited by 24

Relative importance of climate and mountain pine beetle outbreaks on the occurrence of large wildfires in the western USA

Mietkiewicz, N. P., & Kulakowski, D. (2016) · Ecological Applications · doi:10.1002/eap.1400 · Cited by 24

Differentially- and non-differentially-private random decision treesPreprint

Bojarski, M., Choromanska, A., Choromański, K., & LeCun, Y. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1410.6973 · Cited by 24

Making Sense of Random Forest Probabilities: a Kernel PerspectivePreprint

Olson, M. A., & Wyner, A. J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.05792 · Cited by 24

A big data approach to cargo type prediction and its implications for oil trade estimation

Li, Y., Bai, X., Wang, Q., & Ma, Z. (2022) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2022.102831 · Cited by 23

The effect of formation thickness on the performance of deterministic and machine learning models for rate of penetration management in inclined and horizontal wells

Najjarpour, M., Jalalifar, H., & Norouzi-Apourvari, S. (2020) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2020.107160 · Cited by 23

Hough Forest Random Field for Object Recognition and Segmentation

Payet, N., & Todorović, S. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.194 · Cited by 22

Estimating the algorithmic variance of randomized ensembles via the bootstrap

Lopes, M. E. (2019) · The Annals of Statistics · doi:10.1214/18-aos1707 · Cited by 22

A Comparison of Word2Vec, HMM2Vec, and PCA2Vec for Malware Classification

Chandak, A., Lee, W., & Stamp, M. (2020) · Springer eBooks · doi:10.1007/978-3-030-62582-5_11 · Cited by 23

Statistical Issues in Machine Learning

Strobl, C. (2008) · Electronic Theses of LMU Munich (Ludwig-Maximilians-Universität München) · doi:10.5282/edoc.8904 · Cited by 21

Transformation ForestsPreprint

Hothorn, T., & Zeileis, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.02110 · Cited by 21

Conditional canonical correlation estimation based on covariates with random forests

Alakuş, C., Larocque, D., Jacquemont, S., Barlaam, F., Martin, C.-O., Agbogba, K., Lippé, S., & Labbe, A. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab158 · Cited by 20

A Survey of Machine Learning Algorithms and Their Application in Information Security

Stamp, M. (2018) · Computer communications and networks · doi:10.1007/978-3-319-92624-7_2 · Cited by 20

An ensemble method for interval-censored time-to-event data

Yao, W., Frydman, H., & Simonoff, J. S. (2019) · Biostatistics · doi:10.1093/biostatistics/kxz025 · Cited by 20

A Perspective of Machine Learning Approach for the Packet Classification in the Software-Defined Network

Bharathi, I., & Valarmathi, K. (2020) · Intelligent Automation & Soft Computing · doi:10.31209/2020.100000188 · Cited by 20

Hammerstein System Identification With the Nearest Neighbor Algorithm

Greblicki, W., & Pawlak, M. (2017) · IEEE Transactions on Information Theory · doi:10.1109/tit.2017.2694013 · Cited by 20

Sharp Analysis of a Simple Model for Random ForestsPreprint

Klusowski, J. M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.02587 · Cited by 20

Estimation and Inference with Trees and Forests in High DimensionsPreprint

Syrgkanis, V., & Zampetakis, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.03210 · Cited by 20

Improved decision making with similarity based machine learning: applications in chemistry

Lemm, D., von Rudorff, G. F., & von Lilienfeld, O. A. (2023) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ad0fa3 · Cited by 19

Forest-type Regression with General Losses and Robust Forest

Li, A. H., & Martin, A. J. (2017) · International Conference on Machine Learning · Cited by 19

Radial-Basis Function Neural Network Synthesis on the Basis of Decision Tree

Субботин, С. А. (2020) · Optical Memory and Neural Networks · doi:10.3103/s1060992x20010051 · Cited by 19

AN EFFECTIVE BIAS-CORRECTED BAGGING METHOD FOR THE VALUATION OF LARGE VARIABLE ANNUITY PORTFOLIOS

Gweon, H., Li, S., & Mamon, R. S. (2020) · Astin Bulletin · doi:10.1017/asb.2020.28 · Cited by 19

Random Forest Classifier Based ECG Arrhythmia Classification

Vimal, C., & Sathish, B. S. (2010) · International Journal of Healthcare Information Systems and Informatics · doi:10.4018/jhisi.2010040101 · Cited by 19

Random Forests for Regression as a Weighted Sum of ${k}$ -Potential Nearest Neighbors

Fernández-González, P., Bielza, C., & Larrañaga, P. (2019) · IEEE Access · doi:10.1109/access.2019.2900755 · Cited by 17

Bags of Projected Nearest Neighbours: Competitors to Random Forests?Preprint

Hofmeyr, D. P. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2503.09651

Random forest based potential k nearest neighbor classifier and its application in gene expression data

Yang, F. (2012) · Systems Engineering - Theory & Practice · Cited by 4

Flexible tree ensembles with data adaptive splitting rules

Mendis, H. (2026) · Mspace (University of Manitoba)

Weighted KNN Algorithm Based on Random Forests

Zhang, H., & Bu, F. (2019) · International Conference on Machine Learning and Computing (ICMLC) · doi:10.1145/3318299.3318313 · Cited by 3