动态筛选特征并聚类去重，提升随机森林精度与效率
Diversity Conscious Refined Random Forest
- 按信息量剔除低效特征，动态生成新树
- 在相同树数下，8个数据集上准确率均优于标准随机森林
- 适合对模型效率和精度有要求的场景
随机森林（RF）是广泛应用的集成学习方法，但通常依赖数百棵树和全部输入特征，导致推理成本高、模型冗余。本文提出一种精炼随机森林分类器，通过迭代优化：先移除信息量最低的特征，再解析计算应新增的树数量，最后基于相关性聚类剔除冗余树。在8个基准数据集（含二分类与多分类）上的实验表明，该模型在相同树数条件下，分类准确率优于标准随机森林。
原文摘要 · Abstract (English)
Random Forest (RF) is a widely used ensemble learning technique known for its robust classification performance across diverse domains. However, it often relies on hundreds of trees and all input features, leading to high inference cost and model redundancy. In this work, our goal is to grow trees dynamically only on informative features and then enforce maximal diversity by clustering and retaining uncorrelated trees. Therefore, we propose a Refined Random Forest Classifier that iteratively refines itself by first removing the least informative features and then analytically determines how many new trees should be grown, followed by correlation-based clustering to remove redundant trees. The classification accuracy of our model was compared against the standard RF on the same number of trees. Experiments on 8 multiple benchmark datasets, including binary and multiclass datasets, demonstrate that the proposed model achieves improved accuracy compared to standard RF.
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