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| Multi-label Learning Model Based on Multi-label Radial Basis Function Neural Network and Regularized Extreme Learning Machine |
| SHAN Dong, XU Xinzheng |
| School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116 |
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Abstract Extreme learning machine(ELM) possesses the characteristics of fast training and good generalization ability compared with radial basis function neural network(RBFNN), and the affinity propagation(AP) clustering algorithm can automatically determine the number of clusters without a prior knowledge. Therefore, a multi-label learning model named ML-AP-RBF-RELM is proposed, and AP clustering algorithm, multi-label back propagation neural network (ML-RBF) and regularized ELM (RELM) are integrated in this model. ML-RBF is used to map in the input layer. In the hidden layer, the number of hidden nodes can be automatically determined by the sum of clustering centers of AP algorithm, and the center of the RBF function can be computed through the center of K-means clustering algorithm while the clustering number is determined by AP algorithm. Finally, the weights from hidden layer to output layer are rapidly calculated through RELM. The simulation results demonstrate that ML-AP-RBF-RELM performs well.
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Received: 02 April 2017
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About author:: (SHAN Dong, born in 1992, master student. His research interests include machine learning and image processing.)
(XU Xinzheng(Corresponding author), born in 1980, Ph.D., associate professor. His research interests include machine learning and image processing.) |
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2017, Vol. 30 
