Multinomial random forest

作者：

Highlights：

• We propose a novel multinomial-based method to improve the greedy splitting process of decision trees.

• We propose a new random forests variant, dubbed multinomial random forest (MRF), based on which we analyze its consistency and privacy-preservation.

• Extensive experiments demonstrate that the performance of MRF is on par with Breiman’s RF and is better than all existing consistent RF variants.

摘要

•We propose a novel multinomial-based method to improve the greedy splitting process of decision trees.•We propose a new random forests variant, dubbed multinomial random forest (MRF), based on which we analyze its consistency and privacy-preservation.•Extensive experiments demonstrate that the performance of MRF is on par with Breiman’s RF and is better than all existing consistent RF variants.

论文关键词：Random forest,Consistency,Differential privacy,Classification

论文评审过程：Received 16 December 2020, Revised 18 August 2021, Accepted 17 September 2021, Available online 20 September 2021, Version of Record 28 September 2021.

论文官网地址：https://doi.org/10.1016/j.patcog.2021.108331