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Proceedings of the 2017 SIAM International Conference on Data Mining

ROFLMAO: Robust Oblique Forests with Linear MAtrix Operations


Random Forest (RF) remains one of the most widely used general purpose classification methods. Two recent large-scale empirical studies demonstrated it to be the best overall classification method among a variety of methods evaluated. One of its main limitations, however, is that it is restricted to only axis-aligned recursive partitions of the feature space. Consequently, RF is particularly sensitive to the orientation of the data. Several studies have proposed “oblique” decision forest methods to address this limitation. However, these methods either have a time and space complexity significantly greater than RF, are sensitive to unit and scale, or empirically do not perform as well as RF on real data. One promising oblique method that was proposed alongside the canonical RF method, called Forest-RC (F-RC), has not received as much attention by the community. Despite it being just as old as RF, virtually no studies exist investigating its theoretical or empirical performance. In this work, we demonstrate that F-RC empirically outperforms RF and another recently proposed oblique method called Random Rotation Random Forest, while approximately maintaining the same computational complexity. Furthermore, a variant of F-RC which rank transforms the data prior to learning is especially invariant to affine transformations and robust to data corruption. Open source code is available.

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cover image Proceedings
Proceedings of the 2017 SIAM International Conference on Data Mining
Pages: 498 - 506
Editors: Nitesh Chawla, University of Notre Dame, Notre Dame, Indiana, USA and Wei Wang, University of California, Los Angeles, California, USA
ISBN (Online): 978-1-61197-497-3


Published online: 9 June 2017




Supported by Defense Advanced Research Projects Agency (DARPA) SIMPLEX program through SPAWAR contract N66001-15-C-4041 and DARPA GRAPHS N66001-14-1-4028

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