Abstract: Multi-label learning deals with the problem that each instance is associated with multiple labels.Existing multi-label learning algorithm IMLL based on lazy learning does not fully consider the distribution of instances.When building the nearest neighbor sets of the instances,the number of the neighbor for each instance is a constant value valued k.It may lead to such an outcome that the instances with higher similarity are ruled out of the nearest neighbor set or the instances with lower similarity are capsulated into the nearest neighbor set,which will affect the performance of the classification method.In this article,an improved multi-label lazy learning algorithm combined with the idea of granular computing was proposed.The nearest neighbor set of each instance is built by the controlling of the granularity.Then the instances in the nearest neighbor set of each instance behave high similarity.Experimental results show that the performance of our algorithm is superior to IMLLA.

Key words: K-nearest-neighbor,Multi-label learning,Lazy learning,IMLLA,Granular computing