Paper Title:
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.

  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, 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
$32.00
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