论文标题
数据驱动的多项式随机森林：具有强稠度的新的随机森林变体
Data-driven multinomial random forest: A new random forest variant with strong consistency
论文作者
论文摘要
在本文中，我们将一些以前一致的随机森林变体的证明方法修改为强烈一致的证明方法，并改善这些变体的数据利用，以获得更好的理论属性和实验性能。 In addition, we propose a data-driven multinomial random forest (DMRF), which has the same complexity with BreimanRF (proposed by Breiman) while satisfying strong consistency with probability 1. It has better performance in classification and regression problems than previous RF variants that only satisfy weak consistency, and in most cases even surpasses BreimanRF in classification tasks.据我们所知，DMRF目前是随机森林的低复杂性和高性能变化，与概率1实现了强大的一致性。
In this paper, we modify 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, we propose a data-driven multinomial random forest (DMRF), which has the same complexity with BreimanRF (proposed by Breiman) while satisfying strong consistency with probability 1. It has better performance in classification and regression problems than previous RF variants that only satisfy weak consistency, and in most cases even surpasses BreimanRF in classification tasks. To the best of our knowledge, DMRF is currently a low-complexity and high-performing variation of random forests that achieves strong consistency with probability 1.
