報(bào) 告 人：張新雨 研究員
報(bào)告題目：Optimal Weighted Random Forests
報(bào)告時(shí)間：2023 年7月14日（周五）上午10：30-11：30
報(bào)告地點(diǎn)：靜遠(yuǎn)樓1506學(xué)術(shù)報(bào)告廳
主辦單位：數(shù)學(xué)研究院、數(shù)學(xué)與統(tǒng)計(jì)學(xué)院、科學(xué)技術(shù)研究院
報(bào)告人簡(jiǎn)介：
張新雨，中科院數(shù)學(xué)與系統(tǒng)科學(xué)研究院預(yù)測(cè)中心研究員。主要從事統(tǒng)計(jì)學(xué)和計(jì)量經(jīng)濟(jì)學(xué)的理論和應(yīng)用研究工作，具體研究方向包括模型平均、機(jī)器學(xué)習(xí)和組合預(yù)測(cè)等，發(fā)表論文80余篇，其中多篇論文發(fā)表在計(jì)量經(jīng)濟(jì)學(xué)和統(tǒng)計(jì)學(xué)頂級(jí)期刊。擔(dān)任SCI期刊《JSSC》領(lǐng)域主編和其他5個(gè)國(guó)內(nèi)外重要期刊的編委，是管理科學(xué)與工程學(xué)會(huì)常務(wù)理事、國(guó)際統(tǒng)計(jì)學(xué)會(huì)當(dāng)選會(huì)員，先后主持自科優(yōu)秀和杰出青年基金項(xiàng)目，曾獲中國(guó)青年科技獎(jiǎng)。
報(bào)告摘要：
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 (1step-WRFopt) and 2 Steps Optimal Weighted RF (2steps-WRFopt), 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 model averaging estimator. Numerical studies conducted on real-world data sets indicate that these algorithms outperform the equal-weight forest and two other weighted RFs proposed in existing literature in most cases.