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Research Article
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Predicting Financial Situation for Companies Through Integration of Adaboost Algorithm and BP Neural Network
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Jianfang Cao,
Junjie Chen
and
Haifang Li
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ABSTRACT
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In study, a prediction model is proposed based on combined
Adaboost algorithm and BP neural network in order to predict companys
financial situation and improve prediction accuracy of BP neural network model.
The significance of solving the problem is to adjust the company's financial
expenditure and make better forecast and analysis for the development of companies.
The proposed method regards BP neural network model as weak predictors and uses
Adaboost algorithm to construct strong predictor, which solves the problems
of local minima defects and slow convergence of BP neural network model. The
core innovation is to construct strong predictor using Adaboost algorithm in
the research. The efficiency of the proposed prediction model is proved by training
and predicting 1350 groups of statistical data of companys
financial situation. The computer simulations have shown that the model is effective
and suitable, has higher forecasting accuracy and is applicable to practice
compared with previous work.
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