Modeling Knowledge Employee’s Turnover Based on P-SVM

Abstract:

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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.

Info:

Periodical:

Advanced Materials Research (Volumes 121-122)

Edited by:

Donald C. Wunsch II, Honghua Tan, Dehuai Zeng, Qi Luo

Pages:

825-831

DOI:

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

Citation:

Y. Zhao and Y. Z. Liu, "Modeling Knowledge Employee’s Turnover Based on P-SVM", Advanced Materials Research, Vols. 121-122, pp. 825-831, 2010

Online since:

June 2010

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Price:

$35.00

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