基于时间中和决策树的鲁棒数据流分类
原标题：Robust Stream Classification using Time Neutralising Decision Trees
AI 摘要
该论文提出 Time Neutralising Trees（TNT）决策树架构，用于概念漂移下的鲁棒数据流分类。TNT 在训练时通过时间分裂判断过滤掉与当前流上下文不再相关的旧样本，并在其重新相关时将其重新纳入，从而无需显式漂移检测器即可自适应概念漂移。作者在 USP DS 仓库的 24 个真实与半真实数据流上评估，结果显示 TNT 在多种漂移类型下取得同类最佳性能，并提供了源代码。
正文节选
Robust Stream Classification using Time Neutralising Decision Trees Abstract Data Streams are characterised as a potentially infinite source of data subject to non-stationarity. This non-stationarity, also called Concept Drift, often results in deployed predictive models having to be perpetually retrained as the underlying data generating process of the stream changes over time. Drift Detectors are a class of methods used to identify these changes, triggering these retraining procedures. Histori