1. Borselli, A., Colla, V., Vannucci, M., Veroli, M.: An image inference system applied to defect detection in flat steel production. IEEE World Congr. Comput. Intell.1, 1–6 (2010)

   Google Scholar 

2. Cateni, S., Colla, V., Vannucci, M.: General purpose input vvariable extraction: a genetic algorithm based procedure give a gap. In: Proceedings of the 9th International Conference on Intelligence Systems design and Applications ISDA’09, pp. 1278–1283 (2009)

   Google Scholar 

3. Cateni, S., Colla, V., Vannucci, M.: Variable selection through genetic algorithms for classification purpose. In: Proceedings of the 10th IASTED International Conference on Artificial Intelligence and Applications, AIA 2010, pp. 6–11 (2010)

   Google Scholar 

4. Cateni, S., Colla, V., Vannucci, M.: A genetic algorithm-based approach for selecting input variables and setting relevant network parameters of som-based classifier. Int. J. Simul. Syst. Sci. Technol.12(2), 30–37 (2011)

   Google Scholar 

5. Cateni, S., Colla, V., Nastasi, G.: A multivariate fuzzy system applied for outliers detection. J. Intell. Fuzzy Syst.24(4), 889–903 (2013)

   MathSciNet  Google Scholar 

6. Cateni, S., Colla, V., Vannucci, M.: A hybrid feature selection method for classification purposes. In: Proceedings on UKSim-AMSS 8th European Modelling Symposium on Computer Modelling and Simulation, EMS 2014, At Pisa (Italy), pp. 39–44 (2014)

   Google Scholar 

7. Duda, R., Hart, P.: Pattern Classification and Scene Analysis. Wiley, New york (1973)

   MATH  Google Scholar 

8. ECSC supported project: Quality improvement for continuously cast steel products by swirling flow strategies. Technical report, Contract number 7210-PR (211)

   Google Scholar 

9. Fageth, R., Allen, W., Jager, U.: Fuzzy logic classification in image processing. Fyuzzy Syst.82(3), 265–278 (1996)

   Article  Google Scholar 

10. Fausett, L.: Foundaments of Neural Networks. Prentice Hall, New York (1994)

    MATH  Google Scholar 

11. Haykin, S.: Self Organizing Maps in Neural Networks—A Comprehensive Foundation, 2nd edn. Prentice Hall (1999)

    Google Scholar 

12. Komperod, M., Hauge, T., Lie, B.: Preprocessing of experimental data for use in model building and model validation. In: The 49th Scandinavian Conference on Simulation and Modeling (2008)

    Google Scholar 

13. Kwon, Y.J., Zhang, J., Lee, H.G.: A cfd based nucleation-growth-removal model for inclusion behaviour in a gas agitated ladle during molten steel deoxidation. ISIJ Int.38, 6 (2008)

    Google Scholar 

14. Lopes, N., Ribeiro, B.: A data pre-processing tool for neural networks (dptnn) use in a moulding injection machine. In: Proceedings of the Second World Manufacturing Congress Durham, pp. 357–361. England (1999)

    Google Scholar 

15. Sheng, D.Y., Soder, M., Alexis: Most relevant mechanism of inclusion growth in an induction-stirred ladle. Scand. J. Matall.31, 210–220 (2002)

    Google Scholar 

16. Sheng, D., Soder, M., Jönsson, P.: J. Scand. J. Metall.31, 134–147 (2002)

    Article  Google Scholar 

17. Sokolova, M., Lapalme, G.: A systsystem analysis of performance measures for classification tasks. Inf. Process. Manage.4, 427–437 (2009)

    Article  Google Scholar 

18. Taguchi, K.: Complex deoxidation equilibria in molten iron by aluminium and calcium. ISIJ Int.45, 1572–1576 (2005)

    Article  Google Scholar 

19. Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)

    MATH  Google Scholar 

20. Zhang, L., Thomas, B.G., Wang, X., Cai, K.: Evaluation and control of steel cleanliness review. In: 85th Steelmaking Conference Warrendale, PA, pp. 431–452 (2002)

    Google Scholar 

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