文章基本信息

标题：Research on Financial Early-warning based on GIHS Improved BP_AdaBoost Algorithm
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作者：Gui-fang Shen ; Yi-Wen Zhang
期刊名称：MATEC Web of Conferences
电子版ISSN：2261-236X
出版年度：2018
卷号：173
DOI：10.1051/matecconf/201817303004
语种：English
出版社：EDP Sciences
摘要：To improve the accuracy of the financial early warning of the company, aiming at defects of slow learning speed, trapped in local solution and inaccurate operating result of the traditional BP neural network with random initial weights and thresholds, a parallel ensemble learning algorithm based on improved harmony search algorithm using good point set (GIHS) optimize the BP_Adaboost is proposed. Firstly, the good-point set is used to construct a more high quality initial harmony library, and it adjusts the parameters dynamically during the search process and generates several solutions in each iteration so as to make full use of information of harmony memory to improve the global search ability and convergence speed of algorithm. Secondly, ten financial indicators are chosen as the inputs of BP neural network value, and GIHS algorithm and BP neural network are combined to construct the parallel ensemble learning algorithm to optimize BP neural network initial weights value and output threshold value. Finally, many of these weak classifier is composed as strong classifier through the AdaBoost algorithm. The improved algorithm is validated in the company's financial early warning. Simulation results show that the performance of GIHS algorithm is better than the basic HS and IHS algorithm, and the GIHS-BP_AdaBoost classifier has higher classification and prediction accuracy.