Soft Computing Techniques for Skills Assessment of Highly Qualified Personnel

  • Héctor Quintián
  • Roberto Vega
  • Vicente Vera
  • Ignacio Aliaga
  • Cristina González Losada
  • Emilio Corchado
  • Fanny Klett
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 239)


This study applies Artificial Intelligence techniques to analyse the results obtained in different tests to assess the skills of high qualified personnel as engineers, pilots, doctors, dentists, etc. Several Exploratory Projection Pursuit techniques are successfully applied to a novel and real dataset for the assessment of personnel skills and to identify weaknesses to be improved in a later phase. These techniques reduce the complexity of the evaluation process and allow identifying the most relevant aspects in the personnel training in an intuitive way, enhancing the particular training process and thus, the human resources management as a whole and saving training costs.


EPP PCA MLHL CMLHL skillsassessments high qualified personnel 


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  1. 1.
    Klett, F., Wang, M.: The War for Talent: Technologies and solutions toward competency and skills development and talent identification (Editorial). Knowledge Management & E-Learning 5(1), 1–9 (2013)Google Scholar
  2. 2.
    Cha, M., Han, S., Lee, J., Choi, B.: A virtual reality based fire training simulator integrated with fire dynamics data. Fire Safety Journal 50, 12–24 (2012)CrossRefGoogle Scholar
  3. 3.
    Rhienmora, P., Haddawy, P., Suebnukarn, S., Dailey, M.N.: Intelligent dental training simulator with objective skill assessment and feedback. Artificial Intelligence in Medicine 52(2), 115–121 (2011)CrossRefGoogle Scholar
  4. 4.
    Jardón, A., Victores, J.G., Martínez, S., Balaguer, C.: Experience acquisition simulator for operating microtuneling boring machines. Automation in Construction 23, 33–46 (2012)CrossRefGoogle Scholar
  5. 5.
    Per Bodin, P., Nylund, M., Battelino, M.: SATSIM—A real-time multi-satellite simulator for test and validation in formation flying projects. Acta Astronautica 74, 29–39 (2012)CrossRefGoogle Scholar
  6. 6.
    Peremezhney, N., Connaughton, C., Unali, G., Hines, E., Lapkin, A.A.: Application of dimensionality reduction to visualisation of high-throughput data and building of a classification model in formulated consumer product design. Chemical Engineering Research and Design 90(12), 2179–2185 (2012)CrossRefGoogle Scholar
  7. 7.
    Song, M., Yang, H., Siadat, S.H., Pechenizkiy, M.: A comparative study of dimensionality reduction techniques to enhance trace clustering performances. Expert Systems with Applications 40(9), 3722–3737 (2013)CrossRefGoogle Scholar
  8. 8.
    Herrero, Á., Zurutuza, U., Corchado, E.: A Neural Visualization IDS For Honeynet Data. International Journal of Neural Systems 22(2) (2012)Google Scholar
  9. 9.
    Vera, V., Corchado, E., Redondo, R., Sedano, J., García, Á.E.: Applying Soft Computing Techniques to Optimise a Dental Milling Process. Neurocomputing 109, 94–104 (2013)CrossRefGoogle Scholar
  10. 10.
    Baruque, B., Corchado, E., Yin, H.: The s(2)-ensemble fusion algorithm. International Journal of Neural Systems 21(6), 505–525 (2011)CrossRefGoogle Scholar
  11. 11.
    Cordon, O., Fernández-Caballero, A., Gámez, J.A., Hoffmann, F.: The impact of soft computing for the progress of artificial intelligence. Applied Soft Computing 11(2), 1491–1492 (2011)CrossRefGoogle Scholar
  12. 12.
    Abraham, A.: Hybrid soft computing and applications. International Journal of Computational Intelligence and Applications 8(1), 5–7 (2009)CrossRefGoogle Scholar
  13. 13.
    Wilk, T., Wozniak, M.: Soft computing methods applied to combination of one-class classifiers. Neurocomputing 75(1), 185–193 (2012)CrossRefGoogle Scholar
  14. 14.
    Kohonen, T.: The self-organizing map. Neurocomputing 21(1-3), 1–6 (1998)CrossRefMATHGoogle Scholar
  15. 15.
    Corchado, E., Baruque, B.: Wevos-visom: An ensemble summarization algorithm for enhanced data visualization. Neurocomputing 75(1), 171–184 (2012)CrossRefGoogle Scholar
  16. 16.
    Sedano, J., de la Cal, E., Curiel, L., Villar, J., Corchado, E.: Soft computing for detecting thermal insulation failures in buildings. In: Proceedings of the 9th International Conference on Computational and Mathematical Methods in Science and Engineering, CMMSE 2009, vol. 4, pp. 1392–1402 (2009)Google Scholar
  17. 17.
    Sedano, J., Curiel, L., Corchado, E., de la Cal, E., Villar, J.: A soft computing based method for detecting lifetime building thermal insulation failures. Integrated Computer-Aided Engineering 17(12), 103–115 (2010)Google Scholar
  18. 18.
    Leray, P., Gallinari, P.: Feature selection with neural networks. Behaviormetrika 26, 145–166 (1999)CrossRefGoogle Scholar
  19. 19.
    Verikas, A., Bacauskiene, M.: Feature selection with neural networks. Pattern Recognition Letters 23(11), 1323–1335 (2002)CrossRefMATHGoogle Scholar
  20. 20.
    Hotelling, H.: Analysis of a complex of statistical variables into principal components. Journal of Education Psychology 24, 417–444 (1933)CrossRefGoogle Scholar
  21. 21.
    Oja, E., Ogawa, H., Wangviwattana, J.: Principal components analysis by homogeneous neural networks, part 1, the weighted subspace criterion. IEICE Transaction on Information and Systems E75D, 366–375 (1992)Google Scholar
  22. 22.
    Krömer, P., Corchado, E., Snášel, V., Platoš, J., García-Hernández, L.: Neural PCA and Maximum Likelihood Hebbian Learning on the GPU. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds.) ICANN 2012, Part II. LNCS, vol. 7553, pp. 132–139. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  23. 23.
    Friedman, J.: Exploratory projection pursuit. Journal of the American Statistical Association 82(397), 249–266 (1987)MathSciNetCrossRefMATHGoogle Scholar
  24. 24.
    Herrero, Á., Corchado, E., SáizBárcena, L., Abraham, A.: DIPKIP: A Connectionist Knowledge Management System to Identify Knowledge Deficits in Practical Cases. Computational Intelligence 26(1), 26–56 (2010)MathSciNetCrossRefMATHGoogle Scholar
  25. 25.
    Corchado, E., Herrero, A.: Neural visualization of network traffic data for intrusion detection. Applied Soft Computing 11(2), 2042–2056 (2011)CrossRefGoogle Scholar
  26. 26.
    Herrero, A., Corchado, E., Gastaldo, P., Zunino, R.: Neural projection techniques for the visual inspection of network traffic. Neurocomputing 72(16-18), 3649–3658 (2009)CrossRefGoogle Scholar
  27. 27.
    Seung, H., Socci, N., Lee, D.: The rectified gaussian distribution. In: Advances in Neural Information Processing Systems, vol. 10, pp. 350–356 (1998)Google Scholar
  28. 28.
    Corchado, E., Herrero, Á.: Neural visualization of network traffic data for intrusion detection. Appl. Soft Comput. 11(2), 2042–2056 (2011)CrossRefGoogle Scholar
  29. 29.
    Bakker, D., Lagerweij, M., Wesselink, P., Vervoorn, M.: Transfer of Manual Dexterity Skills Acquired on the SIMODONT, a Dental Haptic Trainer with a Virtual Environment, to Reality, A Pilot Study. Bio-Algorithms and Med-Systems 6(11), 21–24 (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Héctor Quintián
    • 1
  • Roberto Vega
    • 1
  • Vicente Vera
    • 2
  • Ignacio Aliaga
    • 2
  • Cristina González Losada
    • 2
  • Emilio Corchado
    • 1
  • Fanny Klett
    • 3
  1. 1.University of SalamancaSalamancaSpain
  2. 2.University Complutense of MadridMadridSpain
  3. 3.German Workforce ADL Partnership LaboratoryWaltershausenGermany

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