计算机工程与应用2026，Vol.62Issue(16)：149-159,11.DOI:10.3778/j.issn.1002-8331.2506-0302
RF-NTPP:基于规则融合的神经点过程模型
RF-NTPP:Rule-Fused Neural Temporal Point Process Model
摘要
Abstract
Neural temporal point process(NTPP)models are a classical approach for modeling and predicting event sequences.Addressing issues such as high-frequency event bias caused by class imbalance in existing neural point process models,this paper proposes a rule-fused neural temporal point process model(RF-NTPP).A two-stage rule mining strategy(TSRM)is employed:Logistic regression is used to mine a set of rules that are significantly associated with the target event,and the rules are refined based on model feedback.Rule fusion module RFM(rulefusion module)is used to embed these rules into the network representation,guiding the model to focus on modeling low-frequency and rare events.Temporal positional encoding enhancement module(TPEE)is introduced,enabling adaptive perception of complex time patterns.RF-NTPP is evaluated on three real-world medical datasets Stroke,Coroheart,and Sepsis.It achieves RMSEs of 0.88,1.05,and 0.31 for predicting the timing of low-frequency events,and classification accuracies of 65%,48%,and 69%respectively.All results outperform those of existing mainstream models.关键词
神经点过程/不平衡学习/规则挖掘/规则融合/混合专家模型Key words
neural temporal point process/imbalanced learning/rule mining/rule fusion/mixture of experts分类
信息技术与安全科学引用本文复制引用
王泓烨,林觉凯,曹昀炀,李文浩,金博..RF-NTPP:基于规则融合的神经点过程模型[J].计算机工程与应用,2026,62(16):149-159,11.基金项目
国家自然科学基金(62406270) （62406270）
上海市青年科技启明星计划(24YF2748800). （24YF2748800）
