VSURF: An R Package for Variable Selection Using Random Forests

By Robin Genuer, Jean‐Michel Poggi, Christine Tuleau-Malot. The R Journal, 2015

550 citations recorded by the source corpus.

This paper describes the R package VSURF. Based on random forests, and for both regression and classification problems, it returns two subsets of variables. The first is a subset of important variables including some redundancy which can be relevant for interpretation, and the second one is a smaller subset corresponding to a model trying to avoid redundancy focusing more closely on prediction objective. The two-stage strategy is based on a preliminary ranking of the explanatory variables using the random forests permutation-based score of importance and proceeds using a stepwise forward strategy for variable introduction. The two proposals can be obtained automatically using data-driven default values, good enough to provide interesting results, but can also be tuned by the user. The algorithm is illustrated on a simulated example and its applications to real datasets are presented.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b1084a4703d836f59c9037573119c3d2ae0b319c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Edward Hak-Sing Ip
2. Jaime Lynn Speiser
3. Janet Tooze
4. Hai Yang
5. Michael I. Miller

In Computer Vision and Pattern Recognition

1. Edward Hak-Sing Ip
2. Jaime Lynn Speiser
3. Janet Tooze
4. Eléonore Wolff
5. Michael I. Miller

In Molecular Biology

1. Edward Hak-Sing Ip
2. Jaime Lynn Speiser
3. Janet Tooze
4. Michael I. Miller

Citations in the corpus

• Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling (2019, methodology): “One way to do this would be to apply simple and ef fi cient feature selection procedures before computing a local model with algorithms such as Variable Selection Using Random Forest (VSURF; Genuer et al., 2015) and Recursive Feature Elimination (RFE; Georganos et al., 2017b; Ma et al., 2017).”
• Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach (2017, methodology): “The out-of-bag error can be utilized to calculate the feature importance through the following steps (Genuer et al., 2015): (1) permute the jth variable of X in”
• A Comparison of Random Forest Variable Selection Methods for Classification Prediction Modeling (2019, methodology): “A stepwise selection procedure is implemented in VSURF (Genuer, et al., 2015), whereas RRF (Deng & Runger, 2013) uses a regularized random forest procedure and a forward selection approach.”