Random Forests and Adaptive Nearest Neighbors
Abstract
In this article we study random forests through their connection with a new framework of adaptive nearest-neighbor methods. We introduce a concept of potential nearest neighbors (k-PNNs) and show that random forests can be viewed as adaptively weighted k-PNN methods. Various aspects of random forests can be studied from this perspective. We study the effect of terminal node sizes on the prediction accuracy of random forests. We further show that random forests with adaptive splitting schemes assign weights to k-PNNs in a desirable way: for the estimation at a given target point, these random forests assign voting weights to the k-PNNs of the target point according to the local importance of different input variables. We propose a new simple splitting scheme that achieves desirable adaptivity in a straightforward fashion. This simple scheme can be combined with existing algorithms. The resulting algorithm is computationally faster and gives comparable results. Other possible aspects of random forests, such as using linear combinations in splitting, are also discussed. Simulations and real datasets are used to illustrate the results.
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Lin and Jeon (2006) highlighted a connection between random forests and a particular class of nearest neighbor predictors.
“A critical step was subsequently taken by Lin and Jeon (2006), who highlighted an interesting connection between random forests and a particular class of nearest neighbor predictors, further developed by Biau and Devroye (2010).”
From A random forest guided tour (2016) · cites this paper as Lin and Jeon (2006)
Lin and Jeon (2006) proved that if X is uniformly distributed on [0,1]^p, then provided tree growing is independent of Y_1,...,Y_n, the forest estimate satisfies an inequality involving the maximal number of points in terminal cells.
“This important connection was first pointed out by Lin and Jeon (2006), who proved that if X is uniformly distributed on [0 , 1] p then, provided tree growing is independent of Y 1 , . . . , Y n (such simplified models are sometimes called non-adaptive ), we have”
From A random forest guided tour (2016) · cites this paper as Lin and Jeon (2006)
If exactly one point is left in the leaves and there is no resampling, then no matter what splitting strategy is used, the forest estimate at x is a weighted average of the Y_i whose corresponding X_i are LNN of x.
“Indeed, if exactly one point is left in the leaves and if there is no resampling, then no matter what splitting strategy is used, the forest estimate at x is a weighted average of the Y i whose corresponding X i are LNN of x .”
From A random forest guided tour (2016) · cites this paper as Lin and Jeon (2006)
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731 · see citing works
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 · see citing works
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325 · see citing works
Experiments with a new boosting algorithm
Freund, Y., & Schapire, R. E. (1996) · Cited by 8,189 · see citing works
Multivariate Adaptive Regression Splines
Friedman, J. H. (1991) · The Annals of Statistics · doi:10.1214/aos/1176347963 · Cited by 8,108 · see citing works
Friedman, J., Hastie, T., & Tibshirani, R. (2000) · The Annals of Statistics · doi:10.1214/aos/1016218223 · Cited by 6,884 · see citing works
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 6,874 · see citing works
Pattern Recognition and Neural Networks
Ripley, B. D. (1996) · Cambridge University Press eBooks · doi:10.1017/cbo9780511812651 · Cited by 6,446 · see citing works
The Strength of Weak Learnability
Schapire, R. E. (1990) · Machine Learning · doi:10.1007/bf00116037 · Cited by 4,600 · see citing works
Dietterich, T. G. (2000) · Machine Learning · doi:10.1023/a:1007607513941 · Cited by 2,971 · see citing works
Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties
Fix, E., & Hodges, J. L. (1989) · International Statistical Review · doi:10.2307/1403797 · Cited by 2,563 · see citing works
Boosting a Weak Learning Algorithm by Majority
Freund, Y. (1995) · Information and Computation · doi:10.1006/inco.1995.1136 · Cited by 1,674 · see citing works
Shape Quantization and Recognition with Randomized Trees
Amit, Y., & Geman, D. (1997) · Neural Computation · doi:10.1162/neco.1997.9.7.1545 · Cited by 1,277 · see citing works
Lim, T.-S., Loh, W., & Shih, Y. (2000) · Machine Learning · doi:10.1023/a:1007608224229 · Cited by 1,133 · see citing works
Discriminant adaptive nearest neighbor classification
Hastie, T., & Tibshirani, R. J. (1996) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.506411 · Cited by 833 · see citing works
Optimal Rates of Convergence for Nonparametric Estimators
Stone, C. J. (1980) · The Annals of Statistics · doi:10.1214/aos/1176345206 · Cited by 810 · see citing works
Boosting Algorithms as Gradient Descent
Mason, L., Baxter, J., Bartlett, P. L., & Frean, M. R. (1999) · Cited by 773 · see citing works
Locally adaptive metric nearest-neighbor classification
Domeniconi, C., Peng, J., & Gunopulos, D. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1033219 · Cited by 308 · see citing works
Randomizing Outputs to Increase Prediction Accuracy
Breiman, L. (2000) · Machine Learning · doi:10.1023/a:1007682208299 · Cited by 275 · see citing works
The optimal distance measure for nearest neighbor classification
Short, R. D., & Fukunaga, K. (1981) · IEEE Transactions on Information Theory · doi:10.1109/tit.1981.1056403 · Cited by 243 · see citing works
On bagging and nonlinear estimation
Friedman, J. H., & Hall, P. M. (2006) · Journal of Statistical Planning and Inference · doi:10.1016/j.jspi.2006.06.002 · Cited by 183 · see citing works
The multi-class metric problem in nearest neighbour discrimination rules
Myles, J. P., & Hand, D. J. (1990) · Pattern Recognition · doi:10.1016/0031-3203(90)90123-3 · Cited by 69 · see citing works
Flagged first, then by citations
1 of the 24 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Geurts, P., Ernst, D., & Wehenkel, L. (2006) · Machine Learning · doi:10.1007/s10994-006-6226-1 · Cited by 9,300 · see citing works
Biau, G., & Scornet, E. (2016) · Test · doi:10.1007/s11749-016-0481-7 · Cited by 4,149 · see citing works
Conditional variable importance for random forests
Strobl, C., Boulesteix, A., Kneib, T., Augustin, T., & Zeileis, A. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-307 · Cited by 3,299 · see citing works
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
Wager, S., & Athey, S. (2018) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1319839 · Cited by 2,917 · see citing works
Strobl, C., Malley, J., & Tutz, G. (2009) · Psychological Methods · doi:10.1037/a0016973 · Cited by 2,583 · see citing works
Hyperparameters and tuning strategies for random forest
Probst, P., Wright, M. N., & Boulesteix, A. (2019) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1301 · Cited by 2,319 · see citing works
Athey, S. C., Tibshirani, J., & Wager, S. (2019) · The Annals of Statistics · doi:10.1214/18-aos1709 · Cited by 1,929 · see citing works
Meinshausen, N. (2006) · Cited by 1,522 · see citing works
Menze, B. H., Kelm, B. M., Masuch, R., Himmelreich, U., Bachert, P., Petrich, W. H., & Hamprecht, F. A. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-213 · Cited by 1,315 · see citing works
Cutler, A., Cutler, D. R., & Stevens, J. R. (2012) · Springer eBooks · doi:10.1007/978-1-4419-9326-7_5 · Cited by 1,221 · see citing works
Random forests for genomic data analysis
Chen, X., & Ishwaran, H. (2012) · Genomics · doi:10.1016/j.ygeno.2012.04.003 · Cited by 956 · see citing works
Understanding Random Forests: From Theory to PracticePreprint
Louppe, G. (2014) · arXiv (Cornell University) · doi:10.13140/2.1.1570.5928 · Cited by 940 · see citing works
Tree-Based Batch Mode Reinforcement Learning
Ernst, D., Geurts, P., & Wehenkel, L. A. (2005) · doi:10.5555/1046920.1088690 · Cited by 853 · see citing works
Understanding variable importances in forests of randomized trees
Louppe, G., Wehenkel, L. A., Sutera, A., & Geurts, P. (2013) · ORBi (University of Liège) · Cited by 843 · see citing works
Ren, Y., Zhang, L., & Suganthan, P. N. (2016) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2015.2471235 · Cited by 681 · see citing works
Analysis of a Random Forests ModelPreprint
Gérard Biau, A. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1005.0208 · Cited by 672 · see citing works
Scornet, E., Biau, G., & Vert, J.-P. (2015) · The Annals of Statistics · doi:10.1214/15-aos1321 · Cited by 612 · see citing works
Consistency of Random Forests and Other Averaging Classifiers
Gérard Biau, A., Devroye, L., & Lugosi, G. (2007) · Cited by 457 · see citing works
Rashidi, H. H., Tran, N. K., Betts, E. V., Howell, L. P., & Green, R. (2019) · Academic Pathology · doi:10.1177/2374289519873088 · Cited by 381 · see citing works
Bagging, Boosting and Ensemble Methods
Bühlmann, P. (2011) · Springer eBooks · doi:10.1007/978-3-642-21551-3_33 · Cited by 374 · see citing works
Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife
Wager, S., Hastie, T. J., & Efron, B. (2013) · PubMed · doi:10.48550/arxiv.1311.4555 · Cited by 372 · see citing works
Xiong, Z., Cui, Y., Liu, Z., Zhao, Y., Hu, M., & Hu, J. (2019) · Computational Materials Science · doi:10.1016/j.commatsci.2019.109203 · Cited by 342 · see citing works
Random Forests and Kernel Methods
Scornet, E. (2016) · IEEE Transactions on Information Theory · doi:10.1109/tit.2016.2514489 · Cited by 293 · see citing works
Segal, M. R., & Xiao, Y. (2011) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.12 · Cited by 271 · see citing works
Random Forests for Genetic Association Studies
Goldstein, B. A., Polley, E. C., & Briggs, F. B. S. (2011) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1691 · Cited by 270 · see citing works
Meredig, B., Antono, E., Church, C., Hutchinson, M., Ling, J., Paradiso, S. P., Blaiszik, B. J., Foster, I. T., Gibbons, B. M., Hattrick-Simpers, J. R., et al. (2018) · Molecular Systems Design & Engineering · doi:10.1039/c8me00012c · Cited by 270 · see citing works
Consistency of random survival forests
Ishwaran, H., & Kogalur, U. B. (2010) · Statistics & Probability Letters · doi:10.1016/j.spl.2010.02.020 · Cited by 269 · see citing works
Malley, J. D., Kruppa, J., Dasgupta, A., Malley, K. G., & Ziegler, A. (2011) · Methods of Information in Medicine · doi:10.3414/me00-01-0052 · Cited by 242 · see citing works
Review of Machine Learning Algorithms for Diagnosing Mental Illness
Cho, G., Yim, J., Choi, Y., Ko, J., & Lee, S. H. (2019) · Psychiatry Investigation · doi:10.30773/pi.2018.12.21.2 · Cited by 237 · see citing works
The Effect of Splitting on Random Forests
Ishwaran, H. (2014) · Machine-mediated learning · doi:10.1007/s10994-014-5451-2 · Cited by 210 · see citing works
Machine learning in agricultural and applied economics
Storm, H., Baylis, K., & Heckelei, T. (2019) · European Review of Agricultural Economics · doi:10.1093/erae/jbz033 · Cited by 227 · see citing works
Menze, B. H., Kelm, B. M., Splitthoff, D. N., Köthe, U., & Hamprecht, F. A. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23783-6_29 · Cited by 226 · see citing works
Bias-corrected random forests in regression
Zhang, G., & Lu, Y. (2011) · Journal of Applied Statistics · doi:10.1080/02664763.2011.578621 · Cited by 224 · see citing works
A Brief Survey on Random Forest Ensembles in Classification Model
Shaik, A. B., & Srinivasan, S. (2018) · Lecture notes in networks and systems · doi:10.1007/978-981-13-2354-6_27 · Cited by 219 · see citing works
Chirici, G., Mura, M., McInerney, D. O., Py, N., Tomppo, E., Waser, L. T., Travaglini, D., & McRoberts, R. E. (2016) · Remote Sensing of Environment · doi:10.1016/j.rse.2016.02.001 · Cited by 212 · see citing works
Quantifying Uncertainty in Random Forests via Confidence Intervals and Hypothesis TestsPreprint
MentchLucas, & HookerGiles (2014) · arXiv (Cornell University) · doi:10.5555/2946645.2946671 · Cited by 209 · see citing works
Prasad, R., Deo, R. C., Li, Y., & Maraseni, T. (2018) · Geoderma · doi:10.1016/j.geoderma.2018.05.035 · Cited by 208 · see citing works
Targeting predictors in random forest regression
Borup, D., Christensen, B. J., Mühlbach, N. S., & Nielsen, M. S. (2020) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2022.02.010 · Cited by 207 · see citing works
A new variable importance measure for random forests with missing data
Hapfelmeier, A., Hothorn, T., Ulm, K., & Strobl, C. (2012) · Statistics and Computing · doi:10.1007/s11222-012-9349-1 · Cited by 202 · see citing works
Geometry- and Accuracy-Preserving Random Forest Proximities
Rhodes, J. S., Cutler, A., & Moon, K. R. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3263774 · Cited by 201 · see citing works
Narrowing the Gap: Random Forests In Theory and In PracticePreprint
Denil, M., Matheson, D., & de Freitas, N. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1310.1415 · Cited by 200 · see citing works
Prasad, R., Ali, M., Kwan, P., & Khan, H. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.12.034 · Cited by 199 · see citing works
Navigating Random Forests and related advances in algorithmic modeling
Siroky, D. S. (2009) · Statistics Surveys · doi:10.1214/07-ss033 · Cited by 198 · see citing works
Qoala-T: A supervised-learning tool for quality control of FreeSurfer segmented MRI data
Klapwijk, E. T., van de Kamp, F., van der Meulen, M., Peters, S., & Wierenga, L. M. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.01.014 · Cited by 192 · see citing works
Stratified sampling for feature subspace selection in random forests for high dimensional data
Ye, Y., Wu, Q., Huang, J. Z., Ng, M. K., & Li, X. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2012.09.005 · Cited by 185 · see citing works
Conditional permutation importance revisited
Debeer, D., & Strobl, C. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-03622-2 · Cited by 183 · see citing works
Berk, R., Sherman, L., Barnes, G., Kurtz, E., & Ahlman, L. (2009) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/j.1467-985x.2008.00556.x · Cited by 178 · see citing works
Bootstrap Aggregating and Random Forest
Lee, T., Ullah, A., & Wang, R. (2019) · Advanced studies in theoretical and applied econometrics · doi:10.1007/978-3-030-31150-6_13 · Cited by 172 · see citing works
Imputation of Unordered Markers and the Impact on Genomic Selection Accuracy
Rutkoski, J. E., Poland, J., Jannink, J., & Sorrells, M. E. (2013) · G3 Genes Genomes Genetics · doi:10.1534/g3.112.005363 · Cited by 171 · see citing works
Mapping sub-pixel urban expansion in China using MODIS and DMSP/OLS nighttime lights
Huang, X., Schneider, A., & Friedl, M. A. (2016) · Remote Sensing of Environment · doi:10.1016/j.rse.2015.12.042 · Cited by 171 · see citing works
Zhu, R., Zeng, D., & Kosorok, M. R. (2015) · Journal of the American Statistical Association · doi:10.1080/01621459.2015.1036994 · Cited by 167 · see citing works
Streaming Random Patches for Evolving Data Stream Classification
Gomes, H. M., Read, J., & Bifet, A. (2019) · Proceedings · doi:10.1109/icdm.2019.00034 · Cited by 151 · see citing works
Model Agnostic Supervised Local Explanations
Plumb, G., Molitor, D., & Talwalkar, A. (2018) · arXiv · doi:10.48550/arxiv.1807.02910 · Cited by 149 · see citing works
A machine learning approach to geochemical mapping
Kirkwood, C., Cave, M. R., Beamish, D., Grebby, S. R., & Ferreira, A. (2016) · Journal of Geochemical Exploration · doi:10.1016/j.gexplo.2016.05.003 · Cited by 148 · see citing works
Suárez, J. L., García, S., & Herrera, F. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.08.017 · Cited by 144 · see citing works
Musolf, A. M., Holzinger, E. R., Malley, J. D., & Wilson, J. (2021) · Human Genetics · doi:10.1007/s00439-021-02402-z · Cited by 140 · see citing works
Li, Y., Yan, C. G., Liu, W., & Li, M. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.07.027 · Cited by 139 · see citing works
Ughi, G. J., Adriaenssens, T., Sinnaeve, P., Desmet, W. J., & D’hooge, J. (2013) · Biomedical Optics Express · doi:10.1364/boe.4.001014 · Cited by 137 · see citing works
Adaptive Concentration of Regression Trees, with Application to Random ForestsPreprint
Wager, S., & Walther, G. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1503.06388 · Cited by 136 · see citing works
Yield strength prediction of high-entropy alloys using machine learning
Bhandari, U., Rafi, M. R., Zhang, C., & Yang, S. (2020) · Materials Today Communications · doi:10.1016/j.mtcomm.2020.101871 · Cited by 134 · see citing works
Oblique Decision Tree Ensemble via Multisurface Proximal Support Vector Machine
Zhang, L., & Suganthan, P. N. (2014) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2014.2366468 · Cited by 133 · see citing works
Random Forest Prediction Intervals
Zhang, H., Zimmerman, J., Nettleton, D. S., & Nordman, D. J. (2019) · The American Statistician · doi:10.1080/00031305.2019.1585288 · Cited by 129 · see citing works
Random KNN feature selection - a fast and stable alternative to Random Forests
Li, S., Harner, E. J., & Adjeroh, D. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-450 · Cited by 128 · see citing works
Boulesteix, A., Bender, A., Bermejo, J. L., & Strobl, C. (2011) · Briefings in Bioinformatics · doi:10.1093/bib/bbr053 · Cited by 128 · see citing works
Constructing Robust Affinity Graphs for Spectral Clustering
Zhu, X., Loy, C. C., & Gong, S. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.188 · Cited by 126 · see citing works
A deep auto-encoder model for gene expression prediction
Xie, R., Wen, J., Quitadamo, A., Cheng, J. J., & Shi, X. M. (2017) · BMC Genomics · doi:10.1186/s12864-017-4226-0 · Cited by 120 · see citing works
Estimating heterogeneous treatment effects with right-censored data via causal survival forests
Cui, Y., Kosorok, M. R., Sverdrup, E., Wager, S., & Zhu, R. (2023) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1093/jrsssb/qkac001 · Cited by 117 · see citing works
Machine learning methodologies versus cardiovascular risk scores, in predicting disease risk
Dimopoulos, A. C., Νικολαϊδου, Μ., Caballero, F. F., Engchuan, W., Niubo, A. S., Arndt, H., Ayuso‐Mateos, J. L., Haro, J. M., Chatterji, S., Georgousopoulou, E. N., et al. (2018) · BMC Medical Research Methodology · doi:10.1186/s12874-018-0644-1 · Cited by 116 · see citing works
Gérard Biau, A., & Devroye, L. (2010) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2010.06.019 · Cited by 115 · see citing works
Improving phase prediction accuracy for high entropy alloys with Machine learning
Risal, S., Zhu, W., Guillén, P., & Sun, L. (2021) · Computational Materials Science · doi:10.1016/j.commatsci.2021.110389 · Cited by 110 · see citing works
Feature analysis of encrypted malicious traffic
Shekhawat, A. S., Troia, F. D., & Stamp, M. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.01.064 · Cited by 107 · see citing works
Meinshausen, N. (2010) · The Annals of Applied Statistics · doi:10.1214/10-aoas367 · Cited by 107 · see citing works
A Novel Consistent Random Forest Framework: Bernoulli Random Forests
Wang, Y., Xia, S., Tang, Q., Wu, J. X., & Xingquan Zhu, A. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2729778 · Cited by 106 · see citing works
Bai, J., Li, Y., Li, J., Yang, X. S., Jiang, Y., & Xia, S. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108331 · Cited by 106 · see citing works
Patange, A. D., & Jegadeeshwaran, R. (2020) · Measurement · doi:10.1016/j.measurement.2020.108649 · Cited by 105 · see citing works
Standard errors for bagged and random forest estimators
Sexton, J., & Laake, P. (2008) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2008.08.007 · Cited by 102 · see citing works
A comparative study of Artificial Intelligence methods for project duration forecasting
Wauters, M., & Vanhoucke, M. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.10.008 · Cited by 98 · see citing works
Kruppa, J., Liu, Y., Biau, G., Kohler, M., König, I. R., Malley, J. D., & Ziegler, A. (2014) · Biometrical Journal · doi:10.1002/bimj.201300068 · Cited by 97 · see citing works
Learning When to Use a Decomposition
Kruber, M., Lübbecke, M. E., & Parmentier, A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-59776-8_16 · Cited by 96 · see citing works
Han, S., Kim, H., & Lee, Y.-S. (2020) · Machine Learning · doi:10.1007/s10994-020-05889-1 · Cited by 96 · see citing works
Random Forests for Spatially Dependent Data
Saha, A., Basu, S., & Datta, A. (2021) · Journal of the American Statistical Association · doi:10.1080/01621459.2021.1950003 · Cited by 94 · see citing works
Oblique and rotation double random forest
Ganaie, M. A., Tanveer, M., Suganthan, P. N., & Snåšel, V. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.06.012 · Cited by 89 · see citing works
Lee, J.-H., Yoon, T., Kwon, S., & Lee, J. (2019) · Applied Sciences · doi:10.3390/app10010129 · Cited by 88 · see citing works
Magnussen, S., Næsset, E., & Gobakken, T. (2009) · Remote Sensing of Environment · doi:10.1016/j.rse.2009.11.007 · Cited by 87 · see citing works
Hosseini, M., Bigtashi, A., & Lee, B. (2020) · Energy and Buildings · doi:10.1016/j.enbuild.2020.110543 · Cited by 86 · see citing works
dos Santos-Silva, C. A., Zupin, L., Oliveira-Lima, M., Vilela, L. M. B., Bezerra-Neto, J. P., Ferreira‐Neto, J. R. C., Ferreira, J. D. C., Silva, R. L. D. O., Pires, C. D. J., Aburjaile, F. F., et al. (2020) · Bioinformatics and Biology Insights · doi:10.1177/1177932220952739 · Cited by 84 · see citing works
Generalized mixed‐effects random forest: A flexible approach to predict university student dropout
Pellagatti, M., Masci, C., Ieva, F., & Paganoni, A. M. (2021) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11505 · Cited by 80 · see citing works
Predicting reaction conditions from limited data through active transfer learning
Shim, E., Kammeraad, J. A., Xu, Z., Tewari, A., Cernak, T., & Zimmerman, P. M. (2022) · Chemical Science · doi:10.1039/d1sc06932b · Cited by 80 · see citing works
Computational resources and tools for antimicrobial peptides
Liu, S., Fan, L., Sun, J., Lao, X., & Zheng, H. (2016) · Journal of Peptide Science · doi:10.1002/psc.2947 · Cited by 78 · see citing works
Trees Weighting Random Forest Method for Classifying High-Dimensional Noisy Data
Li, H. B., Wang, W., Ding, H. W., & Dong, J. (2010) · IEEE International Conference on e-Business Engineering (ICEBE) · doi:10.1109/icebe.2010.99 · Cited by 75 · see citing works
Individual treatment effect prediction for amyotrophic lateral sclerosis patients
Seibold, H., Zeileis, A., & Hothorn, T. (2017) · Statistical Methods in Medical Research · doi:10.1177/0962280217693034 · Cited by 75 · see citing works
Recursively Imputed Survival Trees
Zhu, R., & Kosorok, M. R. (2012) · Journal of the American Statistical Association · doi:10.1080/01621459.2011.637468 · Cited by 74 · see citing works
Statistical Learning for Biomedical Data
Malley, J. D., Malley, K. G., & Pajevic, S. (2011) · Cambridge University Press eBooks · doi:10.1017/cbo9780511975820 · Cited by 73 · see citing works
Collective variable-based enhanced sampling and machine learning
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Breidenbach, J., Nothdurft, A., & Kändler, G. (2010) · European Journal of Forest Research · doi:10.1007/s10342-010-0384-1 · Cited by 69 · see citing works
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Chhabra, G., Vashisht, V., & Ranjan, J. (2017) · Indian Journal of Science and Technology · doi:10.17485/ijst/2017/v10i19/110646 · Cited by 68 · see citing works
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Schlosser, L., Hothorn, T., Stauffer, R., & Zeileis, A. (2019) · The Annals of Applied Statistics · doi:10.1214/19-aoas1247 · Cited by 68 · see citing works
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Salles, T., Gonçalves, M., Rodrigues, V., & Rocha, L. (2018) · Information Systems · doi:10.1016/j.is.2018.05.006 · Cited by 66 · see citing works
- Authors
- Lin, Y., & Jeon, Y.
- Affiliations
- University of Wisconsin–Madison (US)
- ORCID
- Lin, Yi
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 2006-06-01
- Volume
- 101
- Issue
- 474
- Pages
- 578-590
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2047081748
- Cited by
- 507
- References
- 24
- Citations by year
- 2026: 17 · 2025: 31 · 2024: 39 · 2023: 45 · 2022: 35 · 2021: 41 · 2020: 45 · 2019: 49 · 2018: 28 · 2017: 20 · 2016: 24 · 2015: 18 · 2014: 28 · 2013: 14 · 2012: 15
- FWCI
- 6.29
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the American Statistical Association (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Keywords
- random forest, voting weights, feature importance, operator splitting, ensemble learning
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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