Choosing Suitable Similarity Measures to Compare Intuitionistic Fuzzy Sets that Represent Experience-Based Evaluation Sets

Marcelo Loor, Guy De Tré

2015

Abstract

Which similarity measures can be used to compare two Atanassov’s intuitionistic fuzzy sets (IFSs) that respectively represent two experience-based evaluation sets? To find an answer to this question, several similarity measures were tested in comparisons between pairs of IFSs that result from simulations of different experience-based evaluation processes. In such a simulation, a support vector learning algorithm was used to learn how a human editor categorizes newswire stories under a specific scenario and, then, the resulting knowledge was used to evaluate the level to which other newswire stories fit into each of the learned categories. This paper presents our findings about how each of the chosen similarity measures reflected the perceived similarity among the simulated experience-based evaluation sets.

References

  1. Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy sets and Systems, 20(1):87-96.
  2. Atanassov, K. T. (2012). On Intuitionistic Fuzzy Sets Theory, volume 283 of Studies in Fuzziness and Soft Computing. Springer Berlin Heidelberg, Berlin, Heidelberg.
  3. Buckley, C., Salton, G., and Allan, J. (1994). The effect of adding relevance information in a relevance feedback environment. In Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 7894, pages 292-300, New York, NY, USA. Springer-Verlag New York, Inc.
  4. Burges, C. J. (1998). A tutorial on support vector machines for pattern recognition. Data mining and knowledge discovery, 2(2):121-167.
  5. Joachims, T. (1998). Text categorization with support vector machines: Learning with many relevant features. Springer.
  6. Joachims, T. (1999). Making large-scale SVM learning practical. In Schölkopf, B., Burges, C., and Smola, A., editors, Advances in Kernel Methods - Support Vector Learning, chapter 11, pages 169-184. MIT Press, Cambridge, MA.
  7. Lewis, D. D., Yang, Y., Rose, T. G., and Li, F. (2004). RCV1: A new benchmark collection for text categorization research. The Journal of Machine Learning Research, 5:361-397.
  8. Loor, M. and De Tré, G. (2014). Connotation-differential Prints - Comparing What Is Connoted Through (Fuzzy) Evaluations. In Proceedings of the International Conference on Fuzzy Computation Theory and Applications, pages 127-136.
  9. Loor, M. and De Tré, G. (2014). Vector based similarity measure for intuitionistic fuzzy sets. In Atanassov, K. T., BaczyÁski, M., Drewniak, J., Kacprzyk, J., Krawczak, M., Szmidt, E., Wygralak, M., and Zadroz?ny, S., editors, Modern approaches in fuzzy sets, intuitionistic fuzzy sets, generalized nets and related topics : volume I : foundations, pages 105-127. SRI-PAS.
  10. Rose, T., Stevenson, M., and Whitehead, M. (2002). The reuters corpus volume 1-from yesterday's news to tomorrow's language resources. In LREC, volume 2, pages 827-832.
  11. Szmidt, E. (2014). Similarity measures between intuitionistic fuzzy sets. In Distances and Similarities in Intuitionistic Fuzzy Sets, volume 307 of Studies in Fuzziness and Soft Computing, pages 87-129. Springer International Publishing.
  12. Szmidt, E. and Kacprzyk, J. (2000). Distances between intuitionistic fuzzy sets. Fuzzy Sets and Systems, 114(3):505-518.
  13. Szmidt, E. and Kacprzyk, J. (2013). Geometric similarity measures for the intuitionistic fuzzy sets. In 8th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-13), pages 840-847. Atlantis Press.
  14. Tversky, A. (1977). Features of similarity. Psychological review, 84(4):327.
  15. Vapnik, V. N. (1995). The nature of statistical learning theory. Springer-Verlag New York, Inc.
  16. Vapnik, V. N. and Vapnik, V. (1998). Statistical learning theory, volume 1. Wiley New York.
  17. Zadeh, L. (1965). Fuzzy sets. Information and Control, 8(3):338-353.
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Paper Citation


in Harvard Style

Loor M. and De Tré G. (2015). Choosing Suitable Similarity Measures to Compare Intuitionistic Fuzzy Sets that Represent Experience-Based Evaluation Sets . In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: FCTA, (ECTA 2015) ISBN 978-989-758-157-1, pages 57-68. DOI: 10.5220/0005600100570068


in Bibtex Style

@conference{fcta15,
author={Marcelo Loor and Guy De Tré},
title={Choosing Suitable Similarity Measures to Compare Intuitionistic Fuzzy Sets that Represent Experience-Based Evaluation Sets},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: FCTA, (ECTA 2015)},
year={2015},
pages={57-68},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005600100570068},
isbn={978-989-758-157-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: FCTA, (ECTA 2015)
TI - Choosing Suitable Similarity Measures to Compare Intuitionistic Fuzzy Sets that Represent Experience-Based Evaluation Sets
SN - 978-989-758-157-1
AU - Loor M.
AU - De Tré G.
PY - 2015
SP - 57
EP - 68
DO - 10.5220/0005600100570068