User Review Classification Algorithm Based on GBDT and Double-layer Drift Detection
ZHANG Tuyi
LIU Sanmin
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.
Keywords：text data stream classificationconcept drift detectionuser reviewsgradient boosted decision treedata distribution
Publication Date：2025-03-19
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 60-66 )