Bayesian approximate broad learning system with dropout structure
CHEN Tao
WANG Li-jie
LIU Yang
XU Li-li
YU Hai-sheng
Abstract：The existing broad learning system(BLS)and its improved algorithms have a common problem,that is,with the increasing complexity of data in practical scenarios,the network structure becomes extremely complex,resulting in the consumption of computing resources increased greatly.To handle the problem,this paper proposes a Bayesian approximate broad learning system with dropout structure(Dropout-BABLS).Firstly,the dropout algorithm is used to randomly discard the hidden layer nodes of broad learning system.Secondly,by combining the Gaussian regression process and Bayesian theory to approximate the loss function of Dropout on the output results,the objective function of Dropout-BABLS is determined.Next,the augmented Lagrange multiplier method is used to optimize the output weight of the objective function.Finally,the analysis and evaluation of the algorithm 10 sets of regression data of UCI machine learning knowledge base and 6 sets of time series data builted by ourselves.The results show that the developed algorithm by Dropout-BABLS can maintain the corresponding output accuracy and reduce the training time by 25％to 50％.
Keywords：broad learning systemdropoutGaussian processBayesian approximateLagrange multipliersregression analysis
Publication Date：2025-08-30
Online Publishing Date：2025-10-10(First online date of this platform, not the publication date of the document)
Pages：9( 1632-1640 )