The Problem of Redundant Variables in Random Forests

Mariusz Kubus

doi:10.18778/0208-6018.339.01

Authors

Mariusz Kubus Opole University of Technology, Faculty of Production Engineering and Logistics, Department of Mathematics and IT Applications

DOI:

https://doi.org/10.18778/0208-6018.339.01

Keywords:

random forests, redundant variables, feature selection, clustering of features

Abstract

Random forests are currently one of the most preferable methods of supervised learning among practitioners. Their popularity is influenced by the possibility of applying this method without a time consuming pre‑processing step. Random forests can be used for mixed types of features, irrespectively of their distributions. The method is robust to outliers, and feature selection is built into the learning algorithm. However, a decrease of classification accuracy can be observed in the presence of redundant variables. In this paper, we discuss two approaches to the problem of redundant variables. We consider two strategies of searching for best feature subset as well as two formulas of aggregating the features in the clusters. In the empirical experiment, we generate collinear predictors and include them in the real datasets. Dimensionality reduction methods usually improve the accuracy of random forests, but none of them clearly outperforms the others.

Downloads

Download data is not yet available.

References

Breiman L. (1996), Bagging predictors, “Machine Learning”, vol. 24(2), pp. 123–140.
Google Scholar

Breiman L. (2001), Random forests, “Machine Learning”, vol. 45, pp. 5–32.
Google Scholar

Freund Y., Schapire R. E. (1996), Experiments with a new boosting algorithm, Proceedings of the 13th International Conference on Machine Learning, Morgan Kaufmann, San Francisco.
Google Scholar

Gatnar E. (2001), Nieparametryczna metoda dyskryminacji i regresji, Wydawnictwo Naukowe PWN, Warszawa.
Google Scholar

Grabiński T., Wydymus S., Zeliaś A. (1982), Metody doboru zmiennych w modelach ekonometrycznych, Państwowe Wydawnictwo Naukowe PWN, Warszawa.
Google Scholar

Granitto P. M., Furlanello C., Biasioli F., Gasperi F. (2006), Recursive feature elimination with random forest for PTR‑MS analysis of agroindustrial products, “Chemometrics and Intelligent Laboratory Systems”, vol. 83(2), pp. 83–90.
Google Scholar

Gregorutti B., Michel B., Saint‑Pierre P. (2017), Correlation and variable importance in random forests, “Statistics and Computing”, vol. 27, issue 3, pp. 659–678.
Google Scholar

Guyon I., Gunn S., Nikravesh M., Zadeh L. (2006), Feature Extraction: Foundations and Applications, Springer, New York.
Google Scholar

Hall M. (2000), Correlation‑based feature selection for discrete and numeric class machine learning, Proceedings of the 17th International Conference on Machine Learning, Morgan Kaufmann, San Francisco.
Google Scholar

Hapfelmeier A., Ulm K. (2013), A new variable selection approach using Random Forests, “Computational Statistics and Data Analysis”, vol. 60, pp. 50–69.
Google Scholar

Hastie T., Tibshirani R., Friedman J. (2009), The Elements of Statistical Learning: Data Mining. Inference and Prediction, 2nd edition, Springer, New York.
Google Scholar

Korf R. E. (1999), Artificial intelligence search algorithms, [in:] M. J. Atallah, Algorithms and Theory of Computation Handbook, CRC Press, Boca Raton–London–New York–Washington.
Google Scholar

Kursa M. B., Rudnicki W. R. (2010), Feature selection with the Boruta package, “Journal of Statistical Software”, vol. 36, issue 11, pp. 1–13, http://www.jstatsoft.org/v36/i11/ [accessed: 15.02.2018].
Google Scholar

Toloşi L., Lengauer T. (2011), Classification with correlated features: unreliability of feature ranking and solutions, “Bioinformatics”, vol. 27, issue 14, pp. 1986–1994, https://doi.org/10.1093/bioinformatics/btr300.
Google Scholar

Ye Y., Wu Q., Zhexue Huang J., Ng M. K., Li X. (2013), Stratified sampling for feature subspace selection in random forests for high dimensional data, “Pattern Recognition”, vol. 46(3), pp. 769–787, https://doi.org/10.1016/j.patcog.2012.09.005.
Google Scholar

Yu L., Liu H. (2004), Efficient feature selection via analysis of relevance and redundancy, “Journal of Machine Learning Research”, no. 5, pp. 1205–1224.
Google Scholar