Multinomial Random Forest: Toward Consistency and Privacy-Preservation [article]

Yiming Li, Jiawang Bai, Jiawei Li, Xue Yang, Yong Jiang, Chun Li, Shutao Xia
2020 arXiv   pre-print
Despite the impressive performance of random forests (RF), its theoretical properties have not been thoroughly understood. In this paper, we propose a novel RF framework, dubbed multinomial random forest (MRF), to analyze the consistency and privacy-preservation. Instead of deterministic greedy split rule or with simple randomness, the MRF adopts two impurity-based multinomial distributions to randomly select a split feature and a split value respectively. Theoretically, we prove the
more » ... of the proposed MRF and analyze its privacy-preservation within the framework of differential privacy. We also demonstrate with multiple datasets that its performance is on par with the standard RF. To the best of our knowledge, MRF is the first consistent RF variant that has comparable performance to the standard RF.
arXiv:1903.04003v3 fatcat:tzlzd2j575hznc2owyn7vadxm4