Data-driven multinomial random forest
This work addresses the need for more reliable and efficient random forest methods in machine learning, though it appears incremental as it builds on existing variants like MRF and BRF.
The authors tackled the problem of improving random forest variants by strengthening consistency proofs and enhancing data utilization, resulting in the data-driven multinomial random forest (DMRF) algorithm that outperforms previous weakly consistent variants and often surpasses standard random forest in classification and regression tasks.
In this article, we strengthen the proof methods of some previously weakly consistent variants of random forests into strongly consistent proof methods, and improve the data utilization of these variants, in order to obtain better theoretical properties and experimental performance. In addition, based on the multinomial random forest (MRF) and Bernoulli random forest (BRF), we propose a data-driven multinomial random forest (DMRF) algorithm, which has lower complexity than MRF and higher complexity than BRF while satisfying strong consistency. It has better performance in classification and regression problems than previous RF variants that only satisfy weak consistency, and in most cases even surpasses standard random forest. To the best of our knowledge, DMRF is currently the most excellent strongly consistent RF variant with low algorithm complexity