Computer Science ›› 2015, Vol. 42 ›› Issue (4): 316-320.doi: 10.11896/j.issn.1002-137X.2015.04.065

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Multi-label Learning for Improved RBF Neural Networks

LI Shu-ling, LIU Rong and LIU Hong   

  • Online:2018-11-14 Published:2018-11-14

Abstract: A modified multi-label radial basis function (RBF) neural network algorithm that can fully consider the relationship between numbers of sample labels was presented.This improved algorithm is based on the fact that ignoring the relevance between sample labels may cause potential performance loss.The modified algorithm first optimizes the RBF basis function center calculation algorithm in hidden layer,i.e.k-means clustering.AP clustering is used to automatically find k values to obtain the node number of hidden layer and a Huffman tree is constructed to select the initial cluster centers to prevent the k-means clustering results falling into local optimal.Then a label counting vector C that reflects the correlation between the labels is constructed,and it is linearly multiplied with the clustering centers which are obtained through k-means clustering optimization to optimize the RBF basis function center and establish RBF neural network.Experiments using the public multi-label emotion data sets demonstrate the effectiveness of the proposed algorithm.

Key words: Multi-label learning,RBF neural networks,k-means clustering,AP clustering

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