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Predicting Job Performance with a Fuzzy Rule-Based System

Author

Listed:
  • J. Philip Craiger

    (Department of Computer Science & Nebraska University Consortium For Information Assurance, University of Nebraska at Omaha, Omaha, NE 68182-0392, USA)

  • Michael D. Coovert

    (Department of Psychology & Institute for Human Performance, Decision Making, & Cybernetics University of South Florida, USA)

  • Mark S. Teachout

    (Armstrong Laboratory, Brooks Air Force Base, TX 78235-5000, USA)

Abstract

Classification problems affect all organizations. Important decisions affecting an organization's effectiveness include predicting the success of job applicants and the matching and assignment of individuals from a pool of applicants to available positions. In these situations, linear mathematical models are employed to optimize the allocation of an organization's human resources.Use of linear techniques may be problematic, however, when relationships between predictor and criterion are nonlinear. As an alternative, we developed a fuzzy associative memory (FAM: a rule-based system based on fuzzy sets and logic) and used it to derive predictive (classification) equations composed of measures of job experience and job performance. The data consisted of two job experience factors used to predict measures of job performance for four US Air Force job families. The results indicated a nonlinear relationship between experience and performance for three of the four data sets. The overall classification accuracy was similar for the two systems, although the FAM provided better classification for two of the jobs. We discuss the apparent nonlinear relationships between experience and performance, and the advantages and implications of using these systems to develop and describe behavioral models.

Suggested Citation

  • J. Philip Craiger & Michael D. Coovert & Mark S. Teachout, 2003. "Predicting Job Performance with a Fuzzy Rule-Based System," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 2(03), pages 425-444.
    • Handle: RePEc:wsi:ijitdm:v:02:y:2003:i:03:n:s0219622003000744
    DOI: 10.1142/S0219622003000744
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    Citations

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    Cited by:

    1. Yu-Shan Chen & Ke-Chiun Chang, 2010. "Using the fuzzy associative memory (FAM) computation to explore the R&D project performance," Quality & Quantity: International Journal of Methodology, Springer, vol. 44(3), pages 537-549, April.
    2. Carlo Alberto Magni & Stefano Malagoli & Giovanni Mastroleo, 2006. "An Alternative Approach To Firms' Evaluation: Expert Systems And Fuzzy Logic," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 5(01), pages 195-225.
    3. Chun-Hao Chen & Tzung-Pei Hong & Yeong-Chyi Lee & Vincent S. Tseng, 2015. "Finding Active Membership Functions for Genetic-Fuzzy Data Mining," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 14(06), pages 1215-1242, November.
    4. Magni, Carlo Alberto, 2004. "Rating and ranking firms with fuzzy expert systems: the case of Camuzzi," MPRA Paper 5889, University Library of Munich, Germany.
    5. Min-Yuan Cheng & Nhat-Duc Hoang, 2016. "A Self-Adaptive Fuzzy Inference Model Based on Least Squares SVM for Estimating Compressive Strength of Rubberized Concrete," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(03), pages 603-619, May.

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