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Evolving Fuzzy Membership Functions for Soft Skills Assessment Optimization

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Knowledge Management in Organizations (KMO 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1027))

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Abstract

This work proposes the design of a decision support tool able to guide the choices of any company HR manager in the evaluation of the profiles of PhD candidates. This paper is part of an ongoing research in the field of PhD profiling. The novelty here is an evolutionary fuzzy model, based on the Membership Functions (MFs) optimization, used to obtain the soft skills candidate profiles. The general aim of the project is the definition of a set of fuzzy rules that are very similar to those that a HR expert would otherwise have to calculate each time for each selected profile and for each individual skill.

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Author information

Authors and Affiliations

  1. Consortium for the Technology Transfer (C2T), via Nuova Valassina, Carate Brianza, MB, Italy

    Antonia Azzini, Stefania Marrara & Amir Topalović

Authors
  1. Antonia Azzini
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  2. Stefania Marrara
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  3. Amir Topalović
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Corresponding author

Correspondence to Stefania Marrara .

Editor information

Editors and Affiliations

  1. University of Staffordshire, Stoke-on-Trent, UK

    Lorna Uden

  2. National University of Kaohsiung, Kaohsiung, Taiwan

    I-Hsien Ting

  3. University of Salamanca, Salamanca, Spain

    Juan Manuel Corchado

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Azzini, A., Marrara, S., Topalović, A. (2019). Evolving Fuzzy Membership Functions for Soft Skills Assessment Optimization. In: Uden, L., Ting, IH., Corchado, J. (eds) Knowledge Management in Organizations. KMO 2019. Communications in Computer and Information Science, vol 1027. Springer, Cham. https://doi.org/10.1007/978-3-030-21451-7_7

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  • DOI: https://doi.org/10.1007/978-3-030-21451-7_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-21450-0

  • Online ISBN: 978-3-030-21451-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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