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Soft Computing

, Volume 22, Issue 15, pp 5121–5146 | Cite as

A bipolar knowledge representation model to improve supervised fuzzy classification algorithms

  • Guillermo Villarino
  • Daniel Gómez
  • J. Tinguaro Rodríguez
  • Javier Montero
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Abstract

Most supervised classification algorithms produce a soft score (either a probability, a fuzzy degree, a possibility, a cost, etc.) assessing the strength of the association between items and classes. After that, each item is assigned to the class with the highest soft score. In this paper, we show that this last step can be improved through alternative procedures more sensible to the available soft information. To this aim, we propose a general fuzzy bipolar approach that enables learning how to take advantage of the soft information provided by many classification algorithms in order to enhance the generalization power and accuracy of the classifiers. To show the suitability of the proposed approach, we also present some computational experiences for binary classification problems, in which its application to some well-known classifiers as random forest, classification trees and neural networks produces a statistically significant improvement in the performance of the classifiers.

Keywords

Supervised classification models Bipolar models Machine learning Soft information 

Notes

Acknowledgements

This research has been partially supported by the Government of Spain, Grant TIN2015-66471-P and the FPU fellowship Grant 2015/06202 from the Ministry of Education of Spain.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Alcalá R, Alcalá-Fdez J, Herrera F (2007) A proposal for the genetic lateral tuning of linguistic fuzzy systems and its interaction with rule selection. IEEE Trans Fuzzy Syst 15(4):616–635CrossRefMATHGoogle Scholar
  2. Alcalá-Fdez J, Alcala R, Herrera F (2011a) A fuzzy association rule-based classification model for high-dimensional problems with genetic rule selection and lateral tuning. IEEE Trans Fuzzy Syst 19(5):857–872CrossRefGoogle Scholar
  3. Alcalá-Fdez J, Fernandez A, Luengo J, Derrac J, García S, Sánchez L, Herrera F (2011b) KEEL data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J Mult Valued Logic Soft Comput 17(2–3):255–287Google Scholar
  4. Amo A, Montero J, Molina E (2001) Representation of consistent recursive rules. Eur J Oper Res 130:29–53MathSciNetCrossRefMATHGoogle Scholar
  5. Atanassov KT (1999) Intuitionistic fuzzy sets theory and applications. Physica-Verlag, HeidelbergCrossRefMATHGoogle Scholar
  6. Breiman L (1984) Classification and regression trees. Kluwer Academic Publishers, New YorkMATHGoogle Scholar
  7. Breiman L (2001) Random forests. Mach Learn 40:5–32CrossRefMATHGoogle Scholar
  8. Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20:37–46CrossRefGoogle Scholar
  9. Cordon O, del Jesús MJ, Herrera F (1999) A proposal on reasoning methods in fuzzy rule-based classification systems. Int J Approx Reason 20(1):21–45CrossRefGoogle Scholar
  10. Demsar J (2006) Statistical comparisons of classifiers over multiple datasets. J Mach Learn Res 7:1–30MathSciNetMATHGoogle Scholar
  11. Dubois D, Prade H (2002) Possibility theory. Probability theory and multiple-valued logics: a clarification. Ann Math Artif Intell 32:35–66MathSciNetCrossRefMATHGoogle Scholar
  12. Dubois D, Prade H (2006) A bipolar possibilistic representation of knowledge and preferences and its applications. Fuzzy Logic Appl 3849:1–10CrossRefMATHGoogle Scholar
  13. Dubois D, Prade H (2008) An introduction to bipolar representations of information and preference. Int J Intell Syst 23(8):866–877CrossRefMATHGoogle Scholar
  14. Fünkranz J, Hüllermeier E, Loza Mencía E, Brinker K (2008) Multilabel classification via calibrated label ranking. Mach Learn 73(2):133–153CrossRefGoogle Scholar
  15. García S, Herrera F (2008) An extension on “statistical comparisons of classifiers over multiple datasets” for all pairwise comparisons. J Mach Learn Res 9:2677–2694MATHGoogle Scholar
  16. García S, Fernandez A, Luengo J, Herrera F (2010) Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power. Inf Sci 180(10):2044–2064CrossRefGoogle Scholar
  17. Goldberg D (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, BostonMATHGoogle Scholar
  18. Gómez D, Montero J (2004) A discussion on aggregation operators. Kybernetika 40(1):107–120MathSciNetMATHGoogle Scholar
  19. Holland JH (1975) Adaptation in natural and artificial systems. The University of Michigan Press, Ann ArborGoogle Scholar
  20. Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6:65–70MathSciNetMATHGoogle Scholar
  21. Hullermeier E (2005) Fuzzy methods in machine learning and data mining: status and prospects. Fuzzy Sets Syst 156(3):387–406MathSciNetCrossRefGoogle Scholar
  22. Ishibuchi H, Yamamoto T, Nakashima T (2005) Hybridization of fuzzy GBML approaches for pattern classification problems. IEEE Trans Syst Man Cybern Part B Cybern 35(2):359–365CrossRefGoogle Scholar
  23. Kuhn M (2008) Building predictive models in R using the caret package. J Stat Softw 28(5):1–26.  https://doi.org/10.18637/jss.v028.i05 CrossRefGoogle Scholar
  24. Kumar R, Verma R (2012) Classification algorithms for data mining: a survey. Int J Innov Eng Technol 2:7–14Google Scholar
  25. Lim TS, Loh WY, Shih YS (2000) A comparison of prediction accuracy. Complexity, and training time of thirty-three old and new classification algorithms. Mach Learn 40:203–228CrossRefMATHGoogle Scholar
  26. Montero J, Gómez D, Bustince H (2007) On the relevance of some families of fuzzy Sets. Fuzzy Sets Syst 158(22):2429–2442MathSciNetCrossRefMATHGoogle Scholar
  27. Montero J, Bustince H, Franco C, Rodríguez JT, Gómez D, Pagola M, Fernandez J, Barrenechea E (2016) Paired structures in knowledge representation. Knowl Based Syst 100:50–58CrossRefGoogle Scholar
  28. Osgood CE, Suci GJ, Tannenbaum PH (1957) The measurement of meaning. University of Illinois Press, UrbanaGoogle Scholar
  29. Ozturk M, Tsoukiàs A (2007) Modeling uncertain positive and negative reasons in decision aiding. Decis Support Syst 43:1512–1526CrossRefGoogle Scholar
  30. Ripley BD (1996) Pattern recognition and neural networks. Cambridge University Press, CambridgeCrossRefMATHGoogle Scholar
  31. Rodríguez JT, Vitoriano B, Montero J (2011) Rule-based classification by means of bipolar criteria. In: IEEE symposium on computational intelligence in multicriteria decision-making (MDCM) vol 2011, p 197–204Google Scholar
  32. Rodríguez JT, Vitoriano B, Montero J (2012) A general methodology for data-based rule building and its application to natural disaster management. Comput Oper Res 39(4):863–873MathSciNetCrossRefMATHGoogle Scholar
  33. Rodríguez JT, Vitoriano B, Gómez D, Montero J (2013) Classification of disasters and emergencies under bipolar knowledge representation. In: Vitoriano B, Montero J, Ruan D (eds) Decision aid models for disaster management and emergencies, Atlantis computational intelligence systems, vol 7, p 209–232Google Scholar
  34. Rodríguez JT, Turunen E, Ruan D, Montero J (2014) Another paraconsistent algebraic semantics for Lukasiewicz–Pavelka logic. Fuzzy Sets Syst 242:132–147MathSciNetCrossRefMATHGoogle Scholar
  35. Rojas K, Gómez D, Montero J, Rodríguez JT, Valdivia A, Paiva F (2014) Development of child’s home environment indexes based on consistent families of aggregation operators with prioritized hierarchical information. Fuzzy Sets Syst 241:41–60MathSciNetCrossRefGoogle Scholar
  36. Sivanandam SN, Deepa SN (2007) Introduction to genetic algorithms. Springer, BerlinMATHGoogle Scholar
  37. Turunen E, Ozturk M, Tsoukiàs A (2010) Paraconsistent semantics for Pavelka style fuzzy sentential logic. Fuzzy Sets Syst 161:1926–1940MathSciNetCrossRefMATHGoogle Scholar
  38. [Venables and RipleyVenables and Ripley2002]Venables Venables WN, Ripley BD (2002) Modern applied statistics with S, 4th edn. Springer, BerlinGoogle Scholar
  39. Villarino G, Gómez D, Rodríguez JT (2017) Improving supervised classification algorithms by a bipolar knowledge representation. In: Advances in fuzzy logic and technology 2017, p 518–529Google Scholar
  40. Wilcoxon F (1945) Individual comparisons by ranking methods. Biometrics 1:80–83MathSciNetCrossRefGoogle Scholar
  41. Willighagen E (2005) Genalg: R based genetic algorithm. http://cran.r-project.org/
  42. Zadeh LA (1988) Fuzzy-logic. Computer 21(4):83–93CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Guillermo Villarino
    • 1
  • Daniel Gómez
    • 1
  • J. Tinguaro Rodríguez
    • 2
  • Javier Montero
    • 2
  1. 1.Facultad de Estudios EstadísticosUniversidad ComplutenseMadridSpain
  2. 2.Facultad de Ciencias MatemáticasUniversidad ComplutenseMadridSpain

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