一种用于数据流分类的递归反向传播算法

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中图分类号：TP181；TP183 文献标志码：A

A Recursive Back Propagation Algorithm for Data Stream Classification

LIU Zhanhua， WEN Yimin， LIU Xiang （School of Computer Science and Information Security & School of Software Engineering， GuilinUniversityofElectronic Technology，Guilin541OO4，Guangxi，China）

Abstract：To enhancethelearning abilityof deep neural network model，a recursive back propagation algorithmfordata streamclassfication was proposedto solvethe problemoflowclasification accuracydue toconcept driftinthe traditional deep neural network.Theproposedalgorithm combined the powerful data stream learning ability ofonlinegradient descent algorithm with the fast convergence characteristic of recursive least square method.When the concept drift occurred in thedata stream，the neural network model wastrained graduallbyusing recursive least square method，after reaching arelativelystable state，online gradient descent algorithm was switched to further trainthedeep neural network model，achieve deeperdata stream learning，andoptimize the clasification performanceoftedeep neural network model. The effctivenessof the proposedalgorithm wasverified insomeartificialdata setsandrealdatasets.Theresults show that the proposed algorithm hasexcelentadaptability toconcept drift，and theaccuracyof datastream clasification exceeds those of many algorithms thatonly use online gradient descent algorithm or recursiveleast square method to train neural network model.

Keywords：onlinedeep learning；online gradient descent algorithm；recursive least square method；back propagation; deep neural network；concept drift

近年来，深度学习在众多应用领域取得了显著成就[1-3]，然而，深度神经网络模型（DNN）的学习面临诸多问题，包括梯度消失、特征重用率下降[4]鞍点和局部最小值问题[5]、庞大的参数调整量、训练过程中内部协变量偏移[6、正则化器选择困难、超参数难以确定等。（剩余12776字）