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On Classification of Linguistic Data—Case Study: Post-operative

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Intelligent Decision Technologies 2016 (IDT 2016)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 56))

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Abstract

This article presents simple yet efficient way to classify Post-operative patient data. The classification task of this database is to determine where patients in a postoperative recovery area should be sent to next. Because hypothermia is a significant concern after surgery, the attributes correspond roughly to the body temperature measurements. What makes classification task difficult here is that the most of the attributes are given by linguistic values. Method proposed in this article starts by representing linguistic variables by suitable numbers, which are later normalized into the values between 0 and 1. Next phase this data is classified using simple similarity classifier. Results are compared to the existing results and method presented in this paper provides mean accuracy of 65.23 % whereas second highest reported result is 62.67 % using similarity classifier with PCA and membership functions.

I feel gratitude to the National Defence University which have given me plenty of time to do my research.

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References

  1. Bouchon-Meunier, B., Rifqi, M.: Bothorel, Sylvie: Towards general measures of comparison of objects. Fuzzy Sets Syst. 84(2), 143–153 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  2. Luukka, P.: PCA for fuzzy data and similarity classifier in building recognition system for post-operative patient data. Expert Syst. Appl. 362(1), 1222–1228 (2009)

    Article  Google Scholar 

  3. UCI ML Rep., various authors.: UCI Repository of Machine Learning Databases network document. http://www.ics.uci.edu/126mlearn/MLRepository.html. Accessed 14 March 2004

  4. Frink Jr., O.: New Algebras of Logic. Am. Math. Monthly 45, 210–219 (1938)

    Article  MathSciNet  MATH  Google Scholar 

  5. Hájek, P.: Metamathematics of Fuzzy Logic. Kluwer Academic Publishers, Dordrecht (1998)

    Book  MATH  Google Scholar 

  6. Klir, G.J., Yuan, B.: Fuzzy Sets and Fuzzy Logic: Theory and Applications, pp. 304–321. Prentice Hall, PTR (1995)

    Google Scholar 

  7. Kundu, S., Chen, J.: Fuzzy logic or Lukasiewicz logic: a clarification. Fuzzy Sets Syst. 95, 369–379 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  8. Saastamoinen K., Könönen V., Luukka P.: A classifier based on the fuzzy similarity in the Łukasiewicz-structure with different metrics. In: Proceedings of the FUZZ-IEEE 2002 Conference, Hawaii USA (2002)

    Google Scholar 

  9. Saastamoinen, K., Luukka, P.: Testing continuous t-norm called Łukasiewicz algebra with different means in classification. In: Proceedings of the FUZZ-IEEE 2003 Conference, St Louis, USA

    Google Scholar 

  10. Saastamoinen, K.: Semantic study of the use of parameterized s-implications and equivalences in comparison. In: Proceedings of the IEEE 2004 Conference on Cybernetic and Intelligent Systems, Singapore

    Google Scholar 

  11. Smets, P., Magrez, P.: Implication in fuzzy logic. Int. J. Approx. Reason. 1(4), 327–347 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  12. Whalen, T.: Parameterized R-implications. Fuzzy Sets Syst. 134(2), 231–281 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  13. Carbonell M., Mas M., Mayor G.: On a class of monotonic extended OWA operators. In: Proceedings of the 6th IEEE International Conference on Fuzzy Systems, vol. 3, pp. 1695–1700 (1997)

    Google Scholar 

  14. Turunen, E.: Mathematics Behind Fuzzy Logic. Advances in Soft Computing. Physica-Verlag, Heidelberg (1999)

    MATH  Google Scholar 

  15. Hastie, T., Tibshirani, R.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics. Springer, New York (2001)

    Book  MATH  Google Scholar 

  16. Goldberg, D.E.: Real-coded genetic algorithms, virtual alphabets, and blocking. Technical Report 9001. University of Illinois at Urbana-Champain (1990)

    Google Scholar 

  17. Mantel, B., Periaux, J., Sefrioui M.: Gradient and genetic optimizers for aerodynamic desing. In: ICIAM 95 Conference, Hamburgh (1995)

    Google Scholar 

  18. Michalewics, Z.: Genetic Algorithms + Data Structures = Evolution Programs Artificial Intelligence. Springer, New York (1992)

    Book  Google Scholar 

  19. Grefenstette, J.J.: Optimization of control parameters for genetic algorithms. IEEE Trans. Syst. Man Cybern. 16(1), 122–128 (1986)

    Article  Google Scholar 

  20. Price, K.V., Storn, R.M., Lampinen, J.A.: Differential Evolution—A Practical Approach to Global Optimization. Natural Computing Series. Springer, Berlin (2005)

    Google Scholar 

  21. Auephanwiriyakul, S., Theera-Umpon, N.: Comparison of linguistic and regular hard C-means in postoperative patient data. J. Adv. Comput. Intell. Intell. Inf. 8(6), 599–605 (2004)

    Google Scholar 

  22. Woolery, L., Grzymala-Busse, J., Summers, S., Budihardjo, A.: The use of machine learning program LERS-LB 2.5 in knowledge acquisition for expert system development in nursing. Comput. Nursing 9(6), 227–234 (1991)

    Google Scholar 

  23. Corder, G.W., Foreman, D.I.: Nonparametric statistics for non-statisticians: a step-by-step approach. Wiley (2009)

    Google Scholar 

  24. Krejcie, R.V., Morgan, D.W.: Determining sample size for research activities. Educ. Psychol. Meas. 30(3), 607–610 (1970)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Military Technology, National Defence University, P.O. Box 7, FI-00861, Helsinki, Finland

    Kalle Saastamoinen

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  1. Kalle Saastamoinen
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Correspondence to Kalle Saastamoinen .

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Editors and Affiliations

  1. Maritime University, Gdynia, Poland

    Ireneusz Czarnowski

  2. Artificial Intelligence Department, Universidad Politécnica de Madrid, Boadilla del Monte, Madrid, Spain

    Alfonso Mateos Caballero

  3. KES International, Shoreham-by-sea, United Kingdom

    Robert J. Howlett

  4. University of Canberra, Canberra, South Australia, Australia

    Lakhmi C. Jain

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Saastamoinen, K. (2016). On Classification of Linguistic Data—Case Study: Post-operative. In: Czarnowski, I., Caballero, A., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies 2016. IDT 2016. Smart Innovation, Systems and Technologies, vol 56. Springer, Cham. https://doi.org/10.1007/978-3-319-39630-9_5

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  • DOI: https://doi.org/10.1007/978-3-319-39630-9_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39629-3

  • Online ISBN: 978-3-319-39630-9

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