Abstract: Traditional detection approaches of concept drift mainly focus on single-label scenarios,however,not enough attention has been paid to the problem of mining from multi-label data streams.But applications of such data streams are common in the real world.These make it necessary to design efficient algorithms to detect concept drift for multi-label data streams.So after particularly analyzing the unique property label dependence of multi-label data streams,the paper proposed an algorithm of detecting concept drift based on the probability of relevance for multi-label data streams.The basic idea originates from the reason of concept drift and it describes the distribution of data streams by using the probability of relevance.Then,it estimates whether the concept drift occurs or not through monitoring the change of distribution between the old data and new data.The final experimental results show that the proposed algorithm can rapidly and accurately detect the concept drift and achieve prospective predictive performance for multi-label evolving stream classification.

Key words: Concept drift,Multi-label,Data streams,Probability of relevance,Classification