讲座题目：Optimal Weighted Random Forests
主讲人：喻达磊 教授
主持人：於州 教授
开始时间：2024-06-21 11:00
讲座地址：普陀校区理科大楼A1514
主办单位：统计学院
报告人简介：
喻达磊，博士（香港城市大学），西安交通大学教授，博士生导师。研究领域为随机效应模型、混合模型以及空间计量模型的模型选择、模型平均和估计理论等。已在包括JRSS-B，JASA和中国科学：数学在内的国内外统计学期刊上发表论文十余篇。主持国家自然科学基金项目三项，入选了云南省中青年学术和技术带头人后备人才。担任过Biometrics, Statistica Sinica, CSDA, SADM，《系统工程理论与实践》和《系统科学与数学》等期刊的匿名审稿人。
报告内容：
The random forest (RF) algorithm has become a very popular prediction method for its great flexibility and promising accuracy. In RF, it is conventional to put equal weights on all the base learners (trees) to aggregate their predictions. However, the predictive performances of different trees within the forest can be very different due to the randomization of the embedded bootstrap sampling and feature selection. In this paper, we focus on RF for regression and propose two optimal weighting algorithms, namely the 1 Step Optimal Weighted RF and 2 Steps Optimal Weighted RF, that combine the base learners through the weights determined by weight choice criteria. Under some regularity conditions, we show that these algorithms are asymptotically optimal in the sense that the resulting squared loss and risk are asymptotically identical to those of the infeasible but best possible weighted RF. Numerical studies conducted on real-world data sets and semi-synthetic data sets indicate that these algorithms outperform the equal-weight forest and two other weighted RFs proposed in existing literature in most cases.
