时 间：2024年6月21日11:00-12:00
地 点：普陀校区理科大楼A1514
报告人：喻达磊 西安交通大学教授
主持人：於州 华东师范大学教授
摘 要：
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.
报告人简介：
喻达磊，博士（香港城市大学），西安交通大学数学与统计学院教授，博士生导师。研究领域为模型选择、模型平均、估计理论和统计极限理论等，一些成果发表在JRSS-B、JASA、JBES和中国科学：数学上。入选了国家高层次青年人才计划和西安交通大学校内青拔A类支持计划。