Personnel classification for human resource allocation - A neural network approach

Research output: Contribution to journalArticle

Abstract

While personnel classification based on personality data is considered as the most important decision-making problem in human resource allocation, the nature of subjective and incomplete evaluation of persons to be classified prevents any quantitative method from being useful. We suggest the use of unsupervised Competitive Learning neural networks with the Input Feature Scaling algorithm to overcome the obstacles that conventional analyses face. Simulations using real data prove the potential of the approach. The performance of the approach is comparable to that of the probabilistic-error-minimizing Bayes classifier.

Original languageEnglish
Pages (from-to)161-172
Number of pages12
JournalInternational Journal of Industrial Engineering : Theory Applications and Practice
Volume3
Issue number3
Publication statusPublished - 1996 Sep 1

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Resource allocation
Personnel
Neural networks
Classifiers
Decision making

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering

Cite this

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abstract = "While personnel classification based on personality data is considered as the most important decision-making problem in human resource allocation, the nature of subjective and incomplete evaluation of persons to be classified prevents any quantitative method from being useful. We suggest the use of unsupervised Competitive Learning neural networks with the Input Feature Scaling algorithm to overcome the obstacles that conventional analyses face. Simulations using real data prove the potential of the approach. The performance of the approach is comparable to that of the probabilistic-error-minimizing Bayes classifier.",
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