The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
The revival of the Gini importance?
Open accessAbstract
Motivation: Random forests are fast, flexible and represent a robust approach to analyze high dimensional data. A key advantage over alternative machine learning algorithms are variable importance measures, which can be used to identify relevant features or perform variable selection. Measures based on the impurity reduction of splits, such as the Gini importance, are popular because they are simple and fast to compute. However, they are biased in favor of variables with many possible split points and high minor allele frequency. Results: We set up a fast approach to debias impurity-based variable importance measures for classification, regression and survival forests. We show that it creates a variable importance measure which is unbiased with regard to the number of categories and minor allele frequency and almost as fast as the standard impurity importance. As a result, it is now possible to compute reliable importance estimates without the extra computing cost of permutations. Further, we combine the importance measure with a fast testing procedure, producing p-values for variable importance with almost no computational overhead to the creation of the random forest. Applications to gene expression and genome-wide association data show that the proposed method is powerful and computationally efficient. Availability and implementation: The procedure is included in the ranger package, available at https://cran.r-project.org/package=ranger and https://github.com/imbs-hl/ranger. Supplementary information: Supplementary data are available at Bioinformatics online.
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Cites
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Gene expression profiling predicts clinical outcome of breast cancer
van 't Veer, L. J., Dai, H., van de Vijver, M. J., He, Y. D., Hart, A. A. M., Mao, M., Peterse, H. L., van der Kooy, K., Marton, M. J., Witteveen, A. T., et al. (2002) · Nature · doi:10.1038/415530a · Cited by 9,536
Unbiased Recursive Partitioning: A Conditional Inference Framework
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Genetic Control of Human Brain Transcript Expression in Alzheimer Disease
Webster, J. A., Gibbs, J. R., Clarke, J., Ray, M., Zhang, W., Holmans, P., Rohrer, K., Zhao, A., Marlowe, L., Kaleem, M., et al. (2009) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2009.03.011 · Cited by 304
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Nicholls, H. L., John, C. R., Watson, D. S., Munroe, P. B., Barnes, M. R., & Cabrera, C. P. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00350 · Cited by 155
Conceptual challenges for interpretable machine learning
Watson, D. S. (2022) · Synthese · doi:10.1007/s11229-022-03485-5 · Cited by 87
Bracconi, M., & Maestri, M. (2020) · Chemical Engineering Journal · doi:10.1016/j.cej.2020.125469 · Cited by 48
Noorchenarboo, M., & Grolinger, K. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2024.115177 · Cited by 38
Ramstein, G. P., & Buckler, E. S. (2022) · Genome Biology · doi:10.1186/s13059-022-02747-2 · Cited by 34
Weirich-Benet, E., Pyrina, M., Jiménez-Esteve, B., Fraenkel, E., Cohen, J., & Domeisen, D. I. V. (2023) · Artificial Intelligence for the Earth Systems · doi:10.1175/aies-d-22-0038.1 · Cited by 33
Simsekler, M. C. E., Rodrigues, C., Qazi, A., Ellahham, S., & Ozonoff, A. (2021) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2020.107416 · Cited by 30
Personalised analytics for rare disease diagnostics
Anderson, D., Baynam, G., Blackwell, J. M., & Lassmann, T. (2019) · Nature Communications · doi:10.1038/s41467-019-13345-5 · Cited by 24
What’s all that racket! Soundscapes, phenology, and biodiversity in estuaries
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Metadata
- Authors
- Nembrini, S., König, I. R., & Wright, M. N.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-05-10
- Volume
- 34
- Issue
- 21
- Pages
- 3711-3718
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2802643674
- Cited by
- 751
- References
- 6
- FWCI
- 37.49
- Type
- journal article
- Language
- en
- Open access
- hybrid
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