Abstract
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.
Highlights
With the development of the market economy in our country, enterprises need to give a comprehensive and timely warning of their financial status before they can calmly deal with various crises and challenges
Financial early warning research methods are divided into qualitative analysis of early warning, quantitative analysis and early warning and data mining analysis and early warning
In the literature [3], firstly, the financial index of the company was reduced based on the neighborhood rough set, and the weights and thresholds of the BP neural network were optimized by the particle swarm optimization algorithm
Summary
With the development of the market economy in our country, enterprises need to give a comprehensive and timely warning of their financial status before they can calmly deal with various crises and challenges. In the literature [3], firstly, the financial index of the company was reduced based on the neighborhood rough set, and the weights and thresholds of the BP neural network were optimized by the particle swarm optimization algorithm. Literature [7] exploited the global search performance of genetic algorithm to optimize BP neural network initial connection weights and output thresholds, and integrated several weak classifiers of BP neural network to construct AdaBoost strong classifier and applied it to traffic event prediction Experimental results show that the integrated algorithm can improve the performance of the original BP weak classifier. Several optimized BP neural network weak classifiers are built into strong classifiers by AdaBoost algorithm and applied to the company's financial early warning
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