Modeling Knowledge Employee’s Turnover Based on P-SVM

Yong Zhao; Ye Zheng Liu

doi:10.4028/www.scientific.net/AMR.121-122.825

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 121-122Modeling Knowledge Employee’s Turnover Based on...

Modeling Knowledge Employee’s Turnover Based on P-SVM

Abstract:

Knowledge employee’s turnover forecast is a multi-criteria decision-making problem involving various factors. In order to forecast accurately turnover of knowledge employees, the potential support vector machines(P-SVM) is introduced to develop a turnover forecast model. In the model development, a chaos algorithm and a genetic algorithm (GA) are employed to optimize P-SVM parameters selection. The simulation results show that the model based on potential support vector machine with chaos not only has much stronger generalization ability but also has the ability of feature selection.

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Periodical:

Advanced Materials Research (Volumes 121-122)

Pages:

825-831

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.121-122.825

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Online since:

June 2010

Authors:

Yong Zhao, Ye Zheng Liu

Keywords:

Chaos, Genetic Algorithm (GA), Knowledgeable Employee, P-SVM, Turnover Forecast

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References

[1] R. D. Iverson, J. A. Pullman: Determinants of voluntary turnover and layoffs in an environment of repeated downsizing following a merger: An event history analysis. Journal of Management, Vol. 26 (2000), pp.977-1003.

DOI: 10.1177/014920630002600510

Google Scholar

[2] V. N. Vapnik: An Overview of Statistical Learning Theory. Neural Networks, IEEE Transactions, Vol. 10 (1999), pp.988-999.

DOI: 10.1109/72.788640

Google Scholar

[3] S. Hochreiter, K. Obermayer: Support vector machines for dyadic data. Neural Computation, Vol. 18 (2006), pp.1472-1510.

DOI: 10.1162/neco.2006.18.6.1472

Google Scholar

[4] R. P. Steel & N. K. Ovalle: A review and meta-analysis of research on the relationship between behavioral intentions and employee turnover. Journal of Applied Psychology. Vol. 69 (1984), pp.637-686.

DOI: 10.1037/0021-9010.69.4.673

Google Scholar

[5] W. Q. Ling, Z. C. Zhang, & L. L. Fang: Study on organizational commitment structure of Chinese employees. Journal of Management Sciences. Vol. 3 (2000), pp.76-81, in Chinese.

Google Scholar

[6] X. F. Yuan, Y. N. Wang: Parameter selection method for SVM based on chaos optimization algorithm. Control and decision-making, Vol. 21 (2006), pp.111-113, in Chinese.

Google Scholar

[7] E. Zou, X. F. Li, J. G. Chen: Chaos control and its optimum application. Changsha: Journal of National University of Defense Technology, (2002), in Chinese.

Google Scholar