摘要： 由于传统的概念漂移检测研究主要针对单标签数据流,对现实中常见的多标签数据流却缺乏足够的关注,多标签数据流概念漂移检测问题有待进一步的研究。因此,通过分析多标签数据流中存在的特殊依赖关系,提出了一种基于概率相关性的多标签数据流概念漂移检测算法。其基本思想是从概念漂移的产生原因出发,利用概率相关性近似描述数据分布来监测新旧数据分布变化,判断概念漂移是否发生。实验结果表明,提出的算法能够比较快速、准确地检测到概念漂移,并在多标签概念漂移数据流分类问题上取得了预期的学习效果。
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