missForest: Nonparametric missing value imputation using random forest
By Daniel J. Stekhoven. 2015
326 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/583badc36f43b3cd761e3f54079cea24f3012959
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Citations in the corpus
- Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines) (2015, methodology): “Thus, we applied point pattern analysis (Diggle, 1983; Boots and Getis, 1988) to evaluate the degrees of spatial randomness of every set of 12 non-prospect locations we have selected and then we used the set with strongest spatial randomness.”
- GAIN: Missing Data Imputation using Generative Adversarial Nets (2018, methodology): “In table 2 we report the RMSE (and its standard deviation) for GAIN and 5 other state-of-the-art imputation methods: MICE (Buuren & Oudshoorn, 2000; Buuren & Groothuis-Oudshoorn, 2011), MissForest (Stekhoven & Bühlmann, 2011), Matrix completion (Matrix) (Mazumder et al., 2010a), Auto-encoder (Gonda”
- Tree Space Prototypes: Another Look at Making Tree Ensembles Interpretable (2016, background): “RF proximity has found a variety of applications, including clustering [46], outlier detection [53], imputation [47], etc.”
- A survey on missing data in machine learning (2021, background): “There are several studies in literature [95, 96], where Random Forests were used for handling missing values.”
- Comparison of Random Forest and Parametric Imputation Models for Imputing Missing Data Using MICE: A CALIBER Study (2014, methodology): “We carried out analyses using R 2.12.1 (27), with the software packages mice 2.12 (4), missForest 1.3 (28), survival 2.36-2 (29), and randomForest 4.6-6 (30).”
- missMDA: A Package for Handling Missing Values in Multivariate Data Analysis (2016, methodology): “(2009)’s method is implemented, as well as the missForest package (Stekhoven 2013) for Stekhoven and Bühlmann (2012)’s method.”