Abstract
The paper studies the problem of fuzzy sets subsethood in a new framework inspired by our previous work on textual information processing. The proposed algorithms for categorizing textual documents are expressed in terms of the (approximate) subsethood of representing them fuzzy sets. Here we focus on the novel aspect of such a subsethood which consists in assuming that the elements of the universe are assigned with some importance degrees. Thus when the degree of approximate subsethood of two fuzzy sets is to be determined then not only membership degrees are taken into account but also the mentioned importance degrees. We study how these importance degrees may be taken into account by a class of approximate subsethood indicators based on the calculus of linguistically quantified propositions with a special emphasis on the Kosko’s indicator.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Allan, J. (ed.): Topic Detection and Tracking: Event-Based Information. Kluwer Academic Publishers, Norwell (2002)
Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. ACM Press and Addison Wesley, New York (1999)
Bandler, W., Kohout, L.: Fuzzy power sets and fuzzy implication operators. Fuzzy Sets Syst. 4, 13–30 (1980)
Bordogna, G., Pasi, G.: Application of fuzzy sets theory to extend Boolean information retrieval. In: Crestani, F., Pasi, G. (eds.) Soft Computing in Information Retrieval, pp. 21–47. Physica Verlag, Heidelberg (2000)
Bosc, P., Claveau, V., Pivert, O., Ughetto, L.: Graded-inclusion-based information retrieval systems. In: Boughanem, M., Berrut, C., Mothe, J., Soule-Dupuy, C. (eds.) Advances in Information Retrieval, pp. 252–263. Springer, Heidelberg (2009)
Bosc, P., Pivert, O.: About approximate inclusion and its axiomatization. Fuzzy Sets Syst. 157(11), 1438–1454 (2006)
Bosc, P., Pivert, O.: On a reinforced fuzzy inclusion and its application to database querying. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds.) Advances on Computational Intelligence, pp. 351–360. Springer, Heidelberg (2012)
D’Alessio, S., Murray, K.A., Schiaffino, R., Kershenbaum, A.: The effect of using hierarchical classifiers in text categorization. In: Mariani, J., Harman, D. (eds.) Computer-Assisted Information Retrieval (Recherche d’Information et ses Applications) - RIAO 2000, 6th International Conference, College de France, France, 12–14 April 2000, Proceedings, pp. 302–313. CID (2000)
Fodor, J., Roubens, M.: Fuzzy Preference Modelling and Multicriteria Decision Support. Series D: System Theory, Knowledge Engineering and Problem Solving. Kluwer Academic Publishers, Dordrecht (1994)
Gajewski, M., Kacprzyk, J., Zadrożny, S.: Topic detection and tracking: a focused survey and a new variant. Informatyka Stosowana 2014(1), 133–147 (2014)
Koller, D., Sahami, M.: Hierarchically classifying documents using very few words. In: Fisher, D.H. (ed.) Proceedings of the Fourteenth International Conference on Machine Learning (ICML 1997), Nashville, Tennessee, USA, 8–12 July 1997, pp. 170–178. Morgan Kaufmann (1997)
Kosko, B.: Fuzzy entropy and conditioning. Inf. Sci. 40(2), 165–174 (1986)
Liau, C.J., Yao, Y.: Information retrieval by possibilistic reasoning. In: Database and Expert Systems Applications. LNCS, vol. 2113, pp. 52–61. Springer (2001)
Losada, D.E., Barreiro, A.: A logical model for information retrieval based on propositional logic and belief revision. Comput. J. 44(5), 410–424 (2001)
Losada, D.E., Barreiro, A.: Embedding term similarity and inverse document frequency into a logical model of information retrieval. J. Am. Soc. Inf. Sci. Technol. (JASIST) 54(4), 285–301 (2003)
Omhover, J.F., Rifqi, M., Detyniecki, M.: Ranking invariance based on similarity measures in document retrieval. In: Proceedings of the Third International Conference on Adaptive Multimedia Retrieval: User, Context, and Feedback, AMR 2005, pp. 55–64. Springer, Heidelberg (2006)
Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34(1), 1–47 (2002). https://doi.org/10.1145/505282.505283
Sinha, D., Dougherty, E.R.: Fuzzification of set inclusion: theory and applications. Fuzzy Sets Syst. 55(1), 15–42 (1993)
Ughetto, L., Claveau, V.: Different interpretations of fuzzy gradual-inclusion-based IR models. In: Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology, pp. 431–438. Atlantis Press (2011)
Weigend, A.S., Wiener, E.D., Pedersen, J.O.: Exploiting hierarchy in text categorization. Inf. Retr. 1(3), 193–216 (1999)
Zadeh, L.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)
Zadeh, L.: A computational approach to fuzzy quantifiers in natural languages. Comput. Math. Appl. 9, 149–184 (1983). https://doi.org/10.1016/0898-1221(83)90013-5
Zadeh, L.A.: Probability measures of fuzzy events. J. Math. Anal. Appl. 23, 421–427 (1968)
Zadrożny, S., Kacprzyk, J.: An extended fuzzy Boolean model of information retrieval revisited. In: The 14th IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2005, pp. 1020–1025. IEEE, Reno, May 2005. https://doi.org/10.1109/FUZZY.2005.1452534
Zadrożny, S., Kacprzyk, J., Gajewski, M.: A solution of the multiaspect text categorization problem by a hybrid HMM and LDA based technique. In: 16th International Conference Information Processing and Management of Uncertainty in Knowledge-Based Systems, Eindhoven, The Netherlands (2016, in press)
Zadrożny, S., Kacprzyk, J., Gajewski, M.: A new two-stage approach to the multiaspect text categorization. In: 2015 IEEE Symposium on Computational Intelligence for Human-Like Intelligence, CIHLI 2015, Cape Town, South Africa, 8–10 December 2015, pp. 1484–1490. IEEE (2015)
Zadrożny, S., Kacprzyk, J., Gajewski, M.: A novel approach to sequence-of-documents focused text categorization using the concept of a degree of fuzzy set subsethood. In: Proceedings of the Annual Conference of the North American Fuzzy Information Processing Society NAFIPS 2015 and 5th World Conference on Soft Computing 2015, Redmond, WA, USA, 17–19 August 2015 (2015)
Zadrożny, S., Kacprzyk, J., Gajewski, M.: A new approach to the multiaspect text categorization by using the support vector machines. In: De Tré, G., Grzegorzewski, P., Kacprzyk, J., Owsiński, J.W., Penczek, W., Zadrożny, S. (eds.) Challenging Problems and Solutions in Intelligent Systems, pp. 261–277. Springer, Heidelberg (2016)
Zadrożny, S., Kacprzyk, J., Gajewski, M.: The problem of first story detection in multiaspect text categorization. In: Kulczycki, P., Kóczy, L.T., Mesiar, R., Kacprzyk, J. (eds.) Information Technology and Computational Physics, pp. 3–18. Springer, Cham (2017)
Zadrożny, S., Kacprzyk, J., Gajewski, M.: A hierarchy-aware approach to the multiaspect text categorization problem. In: Zadeh, L.A., Yager, R.R., Shahbazova, S.N., Reformat, M.Z., Kreinovich, V. (eds.) Recent Developments and the New Direction in Soft-Computing Foundations and Applications: Selected Papers from the 6th World Conference on Soft Computing, May 22–25, 2016, Berkeley, USA, pp. 49–62. Springer, Cham (2018)
Zadrożny, S., Nowacka, K.: Interpretation of the keywords weights in information retrieval: fuzzy logic based approaches. In: 2008 19th International Workshop on Database and Expert Systems Applications, pp. 657–661. IEEE Computer Society, September 2008
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Zadrożny, S., Kacprzyk, J., Gajewski, M., De Tré, G. (2021). On the Use of Fuzzy Sets Weighted Subsethood Indicators in a Text Categorization Problem. In: Atanassov, K., et al. Uncertainty and Imprecision in Decision Making and Decision Support: New Challenges, Solutions and Perspectives. IWIFSGN 2018. Advances in Intelligent Systems and Computing, vol 1081. Springer, Cham. https://doi.org/10.1007/978-3-030-47024-1_33
Download citation
DOI: https://doi.org/10.1007/978-3-030-47024-1_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-47023-4
Online ISBN: 978-3-030-47024-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)