Feature Selection with the Boruta Package
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This article describes a R package Boruta, implementing a novel feature selection algorithm for finding emph{all relevant variables}. The algorithm is designed as a wrapper around a Random Forest classification algorithm. It iteratively removes the features which are proved by a statistical test to be less relevant than random probes. The Boruta package provides a convenient interface to the algorithm. The short description of the algorithm and examples of its application are presented.
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The features retained by the Boruta analyses were in the following categories: mobile phone engagement, activity level recorded by the mobile sensors, and skin conductance and HRV features from the wearables.
“The features that were retained were in the following categories: mobile phone engagement, activity level recorded by the mobile sensors, and skin conductance and HRV features from the wearables (Table 2).”
From Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors (2020) · cites this paper as [43]
Using the Boruta algorithm, 39 features were defined as important for the estimation of the HDRS scores, one feature remained undecided, and the remaining features were identified as irrelevant to the outcome variable.
“Using the Boruta algorithm (43) 39 features were defined as important for the estimation of the HDRS scores, one feature remained undecided, and the remaining features were identified as irrelevant to the outcome variable.”
From Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors (2020) · cites this paper as [43]
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- Authors
- Kursa, M. B., & Rudnicki, W. R.
- Venue
- Journal of Statistical Software
- Publisher
- Foundation for Open Access Statistic
- Published
- 2010
- Volume
- 36
- Issue
- 11
- ISSN
- 1548-7660 (ISSN-L)
- OpenAlex
- W2156665896
- Cited by
- 5,659
- References
- 11
- Citations by year
- 2026: 784 · 2025: 903 · 2024: 821 · 2023: 728 · 2022: 680 · 2021: 624 · 2020: 396 · 2019: 261 · 2018: 192 · 2017: 119 · 2016: 48 · 2015: 30 · 2014: 23 · 2013: 25 · 2012: 11
- FWCI
- 12.80
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Journal of Statistical Software (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Computer Science › Artificial Intelligence › Data Analysis with R
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- feature selection, Boruta algorithm, random forest, wrapper-based feature selection, feature importance, R package, classification
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