自适应加权随机森林，提升分类准确率
Adaptive Forests For Classification
- 根据输入动态分配树模型权重，不再平均加权
- 在20多个真实数据集上超越RF和XGBoost
- 结合优化算法自动调整权重，适合高精度场景
随机森林（RF）和极端梯度提升（XGBoost）是广泛使用且表现优异的分类与回归模型。它们通过组合等权重的CART树实现预测，其中RF随机生成树，而XGBoost则按序构建。本文提出自适应森林（AF），一种新方法，可自适应地选择底层CART模型的权重。AF结合了最优预测策略树（OP2T）框架，为每棵树分配依赖输入的非均匀权重，并利用混合整数优化（MIO）动态优化权重候选，从而提升整体性能。实验表明，AF在20多个真实数据集上的二分类和多分类任务中，持续优于传统RF、XGBoost及其他加权随机森林模型。
原文摘要 · Abstract (English)
Random Forests (RF) and Extreme Gradient Boosting (XGBoost) are two of the most widely used and highly performing classification and regression models. They aggregate equally weighted CART trees, generated randomly in RF or sequentially in XGBoost. In this paper, we propose Adaptive Forests (AF), a novel approach that adaptively selects the weights of the underlying CART models. AF combines (a) the Optimal Predictive-Policy Trees (OP2T) framework to prescribe tailored, input-dependent unequal weights to trees and (b) Mixed Integer Optimization (MIO) to refine weight candidates dynamically, enhancing overall performance. We demonstrate that AF consistently outperforms RF, XGBoost, and other weighted RF in binary and multi-class classification problems over 20+ real-world datasets.
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