Multi-label Learning of Kernel Extreme Learning Machine with Non-Equilibrium Label Completion
CHENG Yu-sheng1,2, ZHAO Da-wei1, WANG Yi-bin1,2, PEI Gen-sheng1
1. School of Computer and Information, Anqing Normal University, Anqing, Anhui 246011, China;
2. The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing, Anhui 246011, China
Abstract: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.
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