北京交通大学学报2018，Vol.42Issue(2)：9-13,45,6.DOI:10.11860/j.issn.1673-0291.2018.02.002
基于GBDT的商品分配层次化预测模型
GBDT based hierarchical model for commodity distribution prediction
摘要
Abstract
Commodity prediction uses the previous commodity information to estimate and infer the future trends of the commodity,and it can be used for carrying out reasonable planning and distribution of commodity.To achieve accurate forecast of merchandise sales,a commodity distribution prediction model (HGBDT) based on Gradient Boosting Decision Tree (GBDT)is proposed.To alleviate the problem of dimensionality curse,we construct a Bagging based hierarchical ensemble learning model.The temporal-spatial property of commodity is exploited for characterizing commodity effectively,which is beneficial to boost the generalization of the learned prediction model.Experimental results on open dataset demonstrate the effectiveness of the proposed method.关键词
决策树/回归模型/GBDT/集成学习Key words
decision tree/regression model/gradient boosting decision tree/ensemble learning分类
信息技术与安全科学引用本文复制引用
朱振峰,汤静远,常冬霞,赵耀..基于GBDT的商品分配层次化预测模型[J].北京交通大学学报,2018,42(2):9-13,45,6.基金项目
国家自然科学基金(61572068,61532005) （61572068,61532005）
教育部新世纪优秀人才支持计划项目(NCET-13-0661) （NCET-13-0661）
中央高校基本科研业务费专项资金(2015JBM039)National Natural Science Foundation of China(61572068,61532005) （2015JBM039）
Program for the New Century Excellent Talents in Universities of China(NCET-13-0661) （NCET-13-0661）
Fundamental Research Funds for the Central Universities(2015JBM039) （2015JBM039）
