湖北民族大学学报（自然科学版）2025，Vol.43Issue(1)：60-66,7.DOI:10.13501/j.cnki.42-1908/n.2025.03.017
基于GBDT和双层漂移检测的用户评论分类算法
User Review Classification Algorithm Based on GBDT and Double-layer Drift Detection
摘要
Abstract
To address the concept drift in user comment data streams and enhance accuracy of algorithm,a user review classification algorithm based on gradient boosted decision tree(GBDT)with double-layer drift detection(GBDT-D3)was proposed.Firstly,potential drifts were rapidly detected by calculating the loss improvement ratio in GBDT algorithm,followed by precise drift verification through monitoring centroid shifts of data chunks upon drift warning.Subsequently,the dual-layer drift detection mechanism effectively reduced false alarms and missed detection in user comment streams while improving classification performance for dynamic text data.Finally,the GBDT algorithm was updated based on drift detection reports to enhance classification stability of algorithm.Experiments were carried out on seven real-world text datasets with user interest drift.The results indicated that GBDT-D3 algorithm significantly outperformed traditional online ensemble learning algorithms in both classification accuracy and operational stability.The GBDT-D3 algorithm efficiently identified the concept drift in user comment streams and substantially improved classification precision,providing an effective solution for dynamic text data stream classification tasks.关键词
文本数据流分类/概念漂移检测/用户评论/梯度提升决策树/数据分布Key words
text data stream classification/concept drift detection/user reviews/gradient boosted decision tree/data distribution分类
计算机与自动化引用本文复制引用
章涂义,刘三民..基于GBDT和双层漂移检测的用户评论分类算法[J].湖北民族大学学报（自然科学版）,2025,43(1):60-66,7.基金项目
安徽省自然科学基金项目(2308085MF220) （2308085MF220）
安徽省高校自然科学研究重点项目(2022AH050972,KJ2021A0516). （2022AH050972,KJ2021A0516）
