通过降低主特征权重提升随机森林多样性，显著提高分类准确率。
Heterogeneous Random Forest
- 构建树时主动降低前序树中主导特征的权重，增强分裂特征多样性。
- 在52个数据集上多数表现优于主流集成方法，尤其在噪声少的数据上优势明显。
- 适合处理特征重要性不均、需减少选择偏差的分类任务。
随机森林（RF）是分类问题中广受青睐的机器学习方法，其效果取决于单棵树的准确性与树间的多样性。本文提出新型异质随机森林（HRF），通过在构建过程中引入异质性来有意义地提升树间多样性。具体做法是：在构造后续树的特征子空间时，降低先前树根节点使用过的特征的权重，使主导特征在后续迭代中更难被重复使用，从而促进分裂特征的多样性。模拟实验表明，HRF有效缓解了集成中树的选择偏差，提升了整体多样性，并在噪声较少的数据集上表现更优。为评估性能，我们在52个真实和合成数据集上对比了多种主流集成方法，结果显示HRF在大多数数据集上准确率更高，展现出稳定优越的表现。
原文摘要 · Abstract (English)
Random forest (RF) stands out as a highly favored machine learning approach for classification problems. The effectiveness of RF hinges on two key factors: the accuracy of individual trees and the diversity among them. In this study, we introduce a novel approach called heterogeneous RF (HRF), designed to enhance tree diversity in a meaningful way. This diversification is achieved by deliberately introducing heterogeneity during the tree construction. Specifically, features used for splitting near the root node of previous trees are assigned lower weights when constructing the feature sub-space of the subsequent trees. As a result, dominant features in the prior trees are less likely to be employed in the next iteration, leading to a more diverse set of splitting features at the nodes. Through simulation studies, it was confirmed that the HRF method effectively mitigates the selection bias of trees within the ensemble, increases the diversity of the ensemble, and demonstrates superior performance on datasets with fewer noise features. To assess the comparative performance of HRF against other widely adopted ensemble methods, we conducted tests on 52 datasets, comprising both real-world and synthetic data. HRF consistently outperformed other ensemble methods in terms of accuracy across the majority of datasets.
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