An analysis of applying artificial neural networks for employee selection

被引:0
|
作者
Kirby, EJ [1 ]
Kwon, O [1 ]
Dufner, D [1 ]
Palmer, J [1 ]
机构
[1] Univ Illinois, Sch Business & Management, Dept Management Informat Syst, Springfield, IL 62794 USA
来源
ASSOCIATION FOR INFORMATION SYSTEMS PROCEEDINGS OF THE AMERICAS CONFERENCE ON INFORMATION SYSTEMS | 1998年
关键词
D O I
暂无
中图分类号
F [经济];
学科分类号
02 ;
摘要
This paper describes the research and development of an artificial neural network system as a decision aid for employee selection. The ability of the artificial neural network to recognize patterns even using noisy data for employee selection and performance evaluation suggests this framework has significant potential advantage over traditional statistical models, such as regression analysis. Further, the neural model eliminates several methodological problems associated with the use of multiple regression, including nonlinearity, incorrect function form specification and heteroskedasticity.
引用
收藏
页码:76 / 77
页数:2
相关论文
共 50 条
  • [31] Applying artificial neural networks for modelling ship speed and fuel consumption
    Tarelko, Wieslaw
    Rudzki, Krzysztof
    NEURAL COMPUTING & APPLICATIONS, 2020, 32 (23): : 17379 - 17395
  • [32] Applying artificial neural networks for fault prediction in optical network links
    Gonçalves, CHR
    Oliveira, M
    Andrade, RMC
    de Castro, MF
    TELECOMMUNICATIONS AND NETWORKING - ICT 2004, 2004, 3124 : 654 - 659
  • [33] Feature Selection Using Sequential Forward Selection and classification applying Artificial Metaplasticity Neural Network
    Marcano-Cedeno, A.
    Quintanilla-Dominguez, J.
    Cortina-Januchs, M. G.
    Andina, D.
    IECON 2010 - 36TH ANNUAL CONFERENCE ON IEEE INDUSTRIAL ELECTRONICS SOCIETY, 2010,
  • [34] Applying artificial neural networks to managing stock subject to erratic demand
    de la Fuente, D
    Pino, R
    Priore, P
    Parreño, J
    Gómez, A
    International Conference on Industrial Logistics 2003, Proceedings, 2003, : 207 - 214
  • [35] Evaluation of machinability in turning of engineering alloys by applying artificial neural networks
    Vaxevanidis, Nikolaos M.
    Kechagias, John D.
    Fountas, Nikolaos A.
    Manolakos, Dimitrios E.
    Open Construction and Building Technology Journal, 2014, 8 (01): : 389 - 399
  • [36] Applying neural networks
    Neelakantan, R
    Guiver, J
    HYDROCARBON PROCESSING, 1998, 77 (09): : 91 - +
  • [37] Applying neural networks
    Neelakantan, R
    HYDROCARBON PROCESSING, 1999, 78 (03): : 51 - 51
  • [38] Applying neural networks
    Kern, AG
    HYDROCARBON PROCESSING, 1998, 77 (12): : 41 - 41
  • [39] Artificial neural networks based on principal component analysis input selection for clinical pattern recognition analysis
    Zhang, Ya Xiong
    TALANTA, 2007, 73 (01) : 68 - 75
  • [40] Artificial neural networks for quasar selection and photometric redshift determination
    Yeche, Ch
    Petitjean, P.
    Rich, J.
    Aubourg, E.
    Busca, N.
    Hamilton, J. -Ch
    Le Goff, J. -M.
    Paris, I.
    Peirani, S.
    Pichon, Ch
    Rollinde, E.
    Vargas-Magana, M.
    ASTRONOMY & ASTROPHYSICS, 2010, 523