single-jc.php

JACIII Vol.13 No.4 pp. 407-415
doi: 10.20965/jaciii.2009.p0407
(2009)

Paper:

Human Resource Selection Based on Performance ClassificationUsingWeighted Support Vector Machine

Qiangwei Wang, Boyang Li, and Jinglu Hu

Graduate School of Information, Production and Systems, Waseda University,
2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan

Received:
November 18, 2008
Accepted:
March 10, 2009
Published:
July 20, 2009
Keywords:
human resource selection, weighted support vector machine, weights generating method
Abstract

Recruitment and selection have the first priority in human resource management. Poor selection decisions can be enormously costly to organization. So a valid decision method is needed. Traditional selection method is based on linear model. However, it is not proper for this intricate nonlinear relationship between applicants and job performance. In this paper, we introduce a new human resource selection system using weighted support vector machine (WSVM) which is fit for the nonlinear problem. It gives the selection process a feedback and improves classification results. Besides, we also proposed a new weight generating method to keep the characteristic of human resource. It reduces the effect of outliers and noise for classification, and distinguishes different importance of selection criterions. Furthermore, different weights are compared for WSVM. Questionnaire survey was issued to acquire dataset. Simulation results show that our proposed selection system is valid for human resource selection; it performs higher classification accuracy than traditional linear method. It can be used to support the decision making in human resource selection. Besides, different weight generating methods are compared for WSVM, our proposed weight generating method obtains better efficiency than standard SVM and class center based weight generating method.

Cite this article as:
Qiangwei Wang, Boyang Li, and Jinglu Hu, “Human Resource Selection Based on Performance ClassificationUsingWeighted Support Vector Machine,” J. Adv. Comput. Intell. Intell. Inform., Vol.13, No.4, pp. 407-415, 2009.
Data files:
References
  1. [1]D. Gary, “Human Resource Management (10th Edition),” Prentice Hall Press, 2005.
  2. [2]C. Alessandro and C. Guido., “An approach to the evaluation of human resources by using fuzzy set theory,” IEEE World Congress on Computational Intelligence., Proc. of the Third IEEE Conf. on Fuzzy Systems, Vol.2, pp. 1165-1170, 1994.
  3. [3]N. A. Ruskova, “Decision support system for human resources appraisal and selection,” Intelligent Systems, pp. 354-357, 2002.
  4. [4]V. Vapnik, “The nature of statistical learning theory,” Springer-Verlag, 1995.
  5. [5]W. Qiangwei, L. Boyang, and H. Jinglu, “Human Resource Selection Based on Performance Classification Using Weighted Support Vector Machine,” Joint 4th Int. Conf. on Soft Computing and Intelligent Systems and 9th Int. Symposium on advanced intelligent System (SCIS&ISIS), pp. 1837-1842, 2008.
  6. [6]W. Qiangwei, H. Jinglu, and Z. Yang, “Weighted Support Vector Machine with Combination Weighting Method for Human Resource Selection,” The 3rd Int. Symposium on Computational Intelligence and Industrial Applications (ISCIIA), pp. 405-413, 2008.
  7. [7]L. Chun-Fu and W. Sheng-De, “Fuzzy Support Vector Machine,” IEEE transactions on Neural Networks, Vol.13, No.2, pp. 464-471, 2002..
  8. [8]M. I. John, “Human Resource Management (9th Edition),” China Machine Press, 2004.
  9. [9]R. Alec, A. Wim, and G. Susan, “Measuring the relationship between managerial competencies and performance,” Journal of Management 2006; 32; 360, 2006.
  10. [10]G. Patty, “Hiring by competency models,” The journal for Quality and Participation, Winter 2006; 29,4, pp. 16-18, 2006.
  11. [11]L. M. Spencer, Jr. and S. M. Spencer, “Competence at work,” New York: John Wiley, 1993.
  12. [12]N. Bhushan and R. Kanwal, “Strategic decision making: applying the nalytic hierarchy process,” London: Springer-Verlag, ISBN 1-8523375-6-7, 2004.
  13. [13] S. L. Tung and S. L. Tang, “A comparison of the Saaty's AHP and modified AHP for right and left eigenvector inconsistency,” European Journal of Operational Research, ELSEVIER, pp. 123-128, 1998.
  14. [14]C. Cortes and V. Vapnik, “Support vector networks,” Machine learning, Vol.20, No.3, pp. 273-297, 1995.
  15. [15]D. Shu-Xin and C. Sheng-Tan, “Weighted support vector machine for classification, Systems,” Man and Cybernetics, 2005 IEEE Int. Conf., pp. 3866-3871, 2005.
  16. [16]Y. Xulei, S. Qing, and C. Aize, “Weighted support vector machine for data classification,” Int. Joint Conf. on Neural Networks, pp. 859-864, 2005.
  17. [17]J. Yinshan, J. Chuangying, and M. Heng, “Auto-weighted support vector machines for training sets with multi-duplicate samples,” ICSP'04, pp. 1447-1450, 2004.
  18. [18]P. Wenjuan, Y. Yong, L. Zhijing, and H. Weiyao, “Audio classification in a weighted SVM,” Communications and Information Technologies, 2007. ISCIT '07. Int. Symposium, pp. 468-472, 2007.
  19. [19]C. Leilei and W. Chengdong, “A fuzzy support vector machine based on geometric model,” World Congress on Intelligence Control and Automation, pp. 1843-1846, 2004.
  20. [20]Z. Xiang, X. Xiaoling, and X. Guangyong, “Fuzzy support vector machine based on affinity among samples,” Journal of Software, Vol.17, No.5, pp. 951-958, 2006.
  21. [21] Wikipedia, http://en.wikipedia.org/wiki/Standard_deviation
  22. [22]C.-C. Chang and C.-J. Lin , “LIBSVM: a library for support vector machine,” http://www.csie.ntu.tw/ cjlin/libsvm, 2007.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Mar. 01, 2021