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Erschienen in:
2019 | OriginalPaper | Buchkapitel
Research on Automatic Estimation Method of College Students’ Employment Rate Based on Internet Big Data Analysis
verfasst von : Xiao-hui Zhang, Li-wei Jia, Fa-wei Zhou
Erschienen in: Advanced Hybrid Information Processing
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Abstract
In order to solve the problem of large error and inaccuracy in employment rate estimation, an automatic employment rate estimation method based on Internet big data analysis is proposed. This method can be divided into four steps: Firstly, the data integration model based on XML middleware is used to select the sample data of employment rate estimation. Secondly, the decision tree C4.5 algorithm is used to classify the attributes of the sample data. Thirdly, the improved KPCA algorithm is used to extract the feature vectors of employment information and calculate the distance between the forecasted samples and all samples. Fourthly, non-linear mapping method is used to transform employment structure data into corner data, and grey theory is used to establish employment rate estimation model. The results show that the average employment rate estimation error of this method is 4.81% lower than that of the statistical method based on support vector machine.