Multi-label Learning of Kernel Extreme Learning Machine with Non-Equilibrium Label Completion

At present, many researchers usually directly add the label confidence matrix as a priori knowledge to the classification model, and do not consider the influence of non-equilibrium prior knowledge on the quality of the label set.Based on this, the method of non-equilibrium parameters is introduced, and the basis confidence matrix obtained from the prior knowledge is non-equilibrium.Therefore, a multi-label learning algorithm is proposed, which uses kernel extreme learning machine with non-equilibrium label completion (KELM-NeLC).Firstly, information entropy is used to measure the correlation between labels which gets the basic label confidence matrix.Secondly, the basic label confidence matrix is improved to construct non-equilibrium label completion matrix by the non-equilibrium parameter.Finally, in order to learn to obtain a more accurate label confidence matrix, the non-equilibrium label completion matrix is introduced with the kernel extreme learning machine to solve the multi-label classification problem.The experimental results of the proposed algorithm in the opening benchmark multi-label datasets show that the KELM-NeLC algorithm has some advantages over other comparative multi-label learning algorithms and the statistical hypothesis test further illustrates the effectiveness of the proposed algorithm. © 2019, Chinese Institute of Electronics. All right reserved.