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International Conference on Artificial Intelligence and Soft Computing

ICAISC 2015: Artificial Intelligence and Soft Computing pp 79–89Cite as

Toward Work Groups Classification Based on Probabilistic Neural Network Approach

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9119))

Abstract

This paper presents the application of some Computational Intelligence methods for obtaining a classifier analysing employees to form work groups. The proposed bio-inspired solution analyses employees using data gathered from their professional attitudes and skills, then suggests how to form groups of human resources within a company that can effectively work together. The same proposed tool provides employers with a fair and effective means for employee evaluation. In our approach, employee profiles are processed by a dedicated Radial Basis Probabilistic Neural Network based classifier, which finds non-explicit custom-created groups. The accuracy of the classifier is very high, revealing the potential efficacy of the proposed bio-inspired classification system.

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Author information

Authors and Affiliations

  1. Department of Mathematics and Informatics, University of Catania, Viale A. Doria 6, 95125, Catania, Italy

    Christian Napoli, Giuseppe Pappalardo & Emiliano Tramontana

  2. Institute of Computational Intelligence, Czestochowa University of Technology, Al. Armii Krajowej 36, 42-200, Czestochowa, Poland

    Robert K. Nowicki & Janusz T. Starczewski

  3. Institute of Mathematics, Silesian University of Technology, Kaszubska 23, 44-101, Gliwice, Poland

    Marcin Woźniak

Authors
  1. Christian Napoli
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  2. Giuseppe Pappalardo
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  3. Emiliano Tramontana
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  4. Robert K. Nowicki
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  5. Janusz T. Starczewski
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  6. Marcin Woźniak
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Corresponding author

Correspondence to Christian Napoli .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  3. Częstochowa University of Technology, Częstochowa, Poland

    Rafal Scherer

  4. AGH University of Science and Technology, Krakow, Poland

    Ryszard Tadeusiewicz

  5. University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  6. University of Louisville, Louisville, Kentucky, USA

    Jacek M. Zurada

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© 2015 Springer International Publishing Switzerland

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Napoli, C., Pappalardo, G., Tramontana, E., Nowicki, R.K., Starczewski, J.T., Woźniak, M. (2015). Toward Work Groups Classification Based on Probabilistic Neural Network Approach. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2015. Lecture Notes in Computer Science(), vol 9119. Springer, Cham. https://doi.org/10.1007/978-3-319-19324-3_8

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  • DOI: https://doi.org/10.1007/978-3-319-19324-3_8

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19323-6

  • Online ISBN: 978-3-319-19324-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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