基于随机标记子集的多标记数据流分类算法

提出了基于随机标记子集的多标记数据流分类算法,其基本思想是在多标记分类过程中,将原始较大的标记集随机地划分为多个较小的标记子集,并针对每个标记子集训练一个概率分类器链.在充分利用标记间依赖关系的同时,又有效地降低了概率分类器链的时间复杂度.同时,在算法中嵌入了自适应滑动窗口算法来检测概念漂移.实验结果表明,同其他算法相比,在大多数数据集合上能够更有效地预测实例的类标集合,更适合概念漂移的环境.

Classification for Multi-label Data Streams Based on Random Labelsets

To address the issue of concept drift,on the basis of considering the dependency between labels,a novel ensemble classifier was introduced based on random labelsets for multi-label data streams.First,it divided the label set into several subsets based on RAkEL algorithm.Then a classifier on each subset was built using probabilistic classifier chain.Moreover,the adaptive windowing algorithm as a change detector was used to deal with concept drift.The experimental results on both synthetic and real-world data streams showed that our method achieves better performance than the previous methods,especially in datasets with concept drifts.