254 related articles for article (PubMed ID: 32968335)
1. A Comparison of Random Forest Variable Selection Methods for Classification Prediction Modeling.
Speiser JL; Miller ME; Tooze J; Ip E
Expert Syst Appl; 2019 Nov; 134():93-101. PubMed ID: 32968335
[TBL] [Abstract][Full Text] [Related]
2. A random forest method with feature selection for developing medical prediction models with clustered and longitudinal data.
Speiser JL
J Biomed Inform; 2021 May; 117():103763. PubMed ID: 33781921
[TBL] [Abstract][Full Text] [Related]
3. A comparative study of forest methods for time-to-event data: variable selection and predictive performance.
Liu Y; Zhou S; Wei H; An S
BMC Med Res Methodol; 2021 Sep; 21(1):193. PubMed ID: 34563138
[TBL] [Abstract][Full Text] [Related]
4. Comparison of variable selection methods for clinical predictive modeling.
Sanchez-Pinto LN; Venable LR; Fahrenbach J; Churpek MM
Int J Med Inform; 2018 Aug; 116():10-17. PubMed ID: 29887230
[TBL] [Abstract][Full Text] [Related]
5. Estimating the Growing Stem Volume of Coniferous Plantations Based on Random Forest Using an Optimized Variable Selection Method.
Jiang F; Kutia M; Sarkissian AJ; Lin H; Long J; Sun H; Wang G
Sensors (Basel); 2020 Dec; 20(24):. PubMed ID: 33348807
[TBL] [Abstract][Full Text] [Related]
6. A multicenter random forest model for effective prognosis prediction in collaborative clinical research network.
Li J; Tian Y; Zhu Y; Zhou T; Li J; Ding K; Li J
Artif Intell Med; 2020 Mar; 103():101814. PubMed ID: 32143809
[TBL] [Abstract][Full Text] [Related]
7. Evaluation of variable selection methods for random forests and omics data sets.
Degenhardt F; Seifert S; Szymczak S
Brief Bioinform; 2019 Mar; 20(2):492-503. PubMed ID: 29045534
[TBL] [Abstract][Full Text] [Related]
8. Feature Extraction for Heroin-Use Classification Using Imbalanced Random Forest Methods.
Beattie M; Nicholson C
Subst Use Misuse; 2021; 56(1):123-130. PubMed ID: 33183142
[TBL] [Abstract][Full Text] [Related]
9. A New Random Forest Algorithm-Based Prediction Model of Post-operative Mortality in Geriatric Patients With Hip Fractures.
Xing F; Luo R; Liu M; Zhou Z; Xiang Z; Duan X
Front Med (Lausanne); 2022; 9():829977. PubMed ID: 35646950
[TBL] [Abstract][Full Text] [Related]
10. Comparison of statistical machine learning models for rectal protocol compliance in prostate external beam radiation therapy.
Jones S; Hargrave C; Deegan T; Holt T; Mengersen K
Med Phys; 2020 Apr; 47(4):1452-1459. PubMed ID: 31981427
[TBL] [Abstract][Full Text] [Related]
11. Random Forest (RF) Wrappers for Waveband Selection and Classification of Hyperspectral Data.
Poona NK; van Niekerk A; Nadel RL; Ismail R
Appl Spectrosc; 2016 Feb; 70(2):322-33. PubMed ID: 26903567
[TBL] [Abstract][Full Text] [Related]
12. Bias in random forest variable importance measures: illustrations, sources and a solution.
Strobl C; Boulesteix AL; Zeileis A; Hothorn T
BMC Bioinformatics; 2007 Jan; 8():25. PubMed ID: 17254353
[TBL] [Abstract][Full Text] [Related]
13. oFVSD: a Python package of optimized forward variable selection decoder for high-dimensional neuroimaging data.
Dang T; Fermin ASR; Machizawa MG
Front Neuroinform; 2023; 17():1266713. PubMed ID: 37829329
[TBL] [Abstract][Full Text] [Related]
14. Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone.
Kim H; Lee S; Lee S; Hong S; Kang H; Kim N
JMIR Mhealth Uhealth; 2019 Oct; 7(10):e14149. PubMed ID: 31621642
[TBL] [Abstract][Full Text] [Related]
15. Variable importance-weighted Random Forests.
Liu Y; Zhao H
Quant Biol; 2017 Dec; 5(4):338-351. PubMed ID: 30034909
[TBL] [Abstract][Full Text] [Related]
16. Machine Learning in Aging: An Example of Developing Prediction Models for Serious Fall Injury in Older Adults.
Speiser JL; Callahan KE; Houston DK; Fanning J; Gill TM; Guralnik JM; Newman AB; Pahor M; Rejeski WJ; Miller ME
J Gerontol A Biol Sci Med Sci; 2021 Mar; 76(4):647-654. PubMed ID: 32498077
[TBL] [Abstract][Full Text] [Related]
17. Prediction of O-glycosylation Sites Using Random Forest and GA-Tuned PSO Technique.
Hassan H; Badr A; Abdelhalim MB
Bioinform Biol Insights; 2015; 9():103-9. PubMed ID: 26244014
[TBL] [Abstract][Full Text] [Related]
18. Evaluating Random Forests for Survival Analysis using Prediction Error Curves.
Mogensen UB; Ishwaran H; Gerds TA
J Stat Softw; 2012 Sep; 50(11):1-23. PubMed ID: 25317082
[TBL] [Abstract][Full Text] [Related]
19. A random forest based computational model for predicting novel lncRNA-disease associations.
Yao D; Zhan X; Zhan X; Kwoh CK; Li P; Wang J
BMC Bioinformatics; 2020 Mar; 21(1):126. PubMed ID: 32216744
[TBL] [Abstract][Full Text] [Related]
20. Variable selection and validation in multivariate modelling.
Shi L; Westerhuis JA; Rosén J; Landberg R; Brunius C
Bioinformatics; 2019 Mar; 35(6):972-980. PubMed ID: 30165467
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
