Research of multi-label learning based on covering algorithm
Ling Zhang · Computer Engineering and Applications Journal · 2010
Multi-label learning is a common problem in real application.Covering algorithm performs well with single-label learning but can not deal with multi-label learning.In this paper,covering algorithm is extended to Multi-label Learning Covering Algorithm(MLCA).Training and testing procedures are adapted to the characteristics of multi-label learning problem,and the membership function of sample is calculated.MLCA is applied to the gene classification and nature scene classification and the results show that MLCA is effective and has better performance than many other learning algorithms in the field.