论文标题
Enhash：用于概念漂移检测的快速流算法
Enhash: A Fast Streaming Algorithm For Concept Drift Detection
论文作者
论文摘要
我们提出了Enhash，这是一个快速的合奏学习者，它在数据流中检测\ textit {概念漂移}。流可以包括突然，渐进，虚拟或重复的事件，或各种漂移的混合物。增强使用投影哈希来插入传入样本。我们从经验上表明，所提出的方法在较少的时间内对现有的集合学习者具有竞争性能。此外，Enhash具有适度的资源要求。与性能比较相关的实验是在6个人造和4个由各种漂移组成的实际数据集上进行的。
We propose Enhash, a fast ensemble learner that detects \textit{concept drift} in a data stream. A stream may consist of abrupt, gradual, virtual, or recurring events, or a mixture of various types of drift. Enhash employs projection hash to insert an incoming sample. We show empirically that the proposed method has competitive performance to existing ensemble learners in much lesser time. Also, Enhash has moderate resource requirements. Experiments relevant to performance comparison were performed on 6 artificial and 4 real data sets consisting of various types of drifts.
