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Linguistic Summaries Using Interval-Valued Fuzzy Representation of Imprecise Information - An Innovative Tool for Detecting Outliers

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Computational Science – ICCS 2021 (ICCS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12747))

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Abstract

The practice of textual and numerical information processing often involves the need to analyze and test a database for the presence of items that differ substantially from other records. Such items, referred to as outliers, can be successfully detected using linguistic summaries. In this paper, we extend this approach by the use of non-monotonic quantifiers and interval-valued fuzzy sets. The results obtained by this innovative method confirm its usefulness for outlier detection, which is of significant practical relevance for database analysis applications.

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References

  1. Shareef, D.M.A.M., Aminifar, S.A.: Uncertainty handling in big data using fuzzy logic-literature review (2021)

    Google Scholar 

  2. Ross, T.J., et al.: Fuzzy Logic with Engineering Applications, vol. 2. Wiley, Hoboken (2004)

    MATH  Google Scholar 

  3. Duraj, A., Szczepaniak, P.S.: Information outliers and their detection. In: Burgin, M., Hofkirchner, W. (eds.) Information Studies and the Quest for Transdisciplinarity, vol. 9, pp. 413–437, Chapter 15. World Scientific Publishing Company (2017)

    Google Scholar 

  4. Hawkins, D.M.: Identification of Outliers. Monographs on Statistics and Applied Probability, vol. 11. Springer, Heidelberg (1980). https://doi.org/10.1007/978-94-015-3994-4

    Book  MATH  Google Scholar 

  5. Hawkins, S., He, H., Williams, G., Baxter, R.: Outlier detection using replicator neural networks. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds.) DaWaK 2002. LNCS, vol. 2454, pp. 170–180. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-46145-0_17

    Chapter  Google Scholar 

  6. Barnett, V., Lewis, T.: Outliers in Statistical Data, vol. 3. Wiley, New York (1994)

    MATH  Google Scholar 

  7. Guevara, J., Canu, S., Hirata, R.: Support measure data description for group anomaly detection. In: ODDx3 Workshop on Outlier Definition, Detection, and Description at the 21st ACM SIGKDD International Conference On Knowledge Discovery And Data Mining (KDD 2015) (2015)

    Google Scholar 

  8. Xiong, L., Póczos, B., Schneider, J., Connolly, A., Vander Plas, J.: Hierarchical probabilistic models for group anomaly detection. In: International Conference on Artificial Intelligence and Statistics 2011, pp. 789–797. Springer (2011)

    Google Scholar 

  9. Jayakumar, G., Thomas, B.J.: A new procedure of clustering based on multivariate outlier detection. J. Data Sci. 11(1), 69–84 (2013)

    Article  MathSciNet  Google Scholar 

  10. Yager, R.R.: A new approach to the summarization of data. Inf. Sci. 28(1), 69–86 (1982)

    Article  MathSciNet  Google Scholar 

  11. Yager, R.R.: Linguistic summaries as a tool for database discovery. In: FQAS, pp. 17–22 (1994)

    Google Scholar 

  12. Yager, R.: Linguistic summaries as a tool for databases discovery. In: Workshop on Fuzzy Databases System and Information Retrieval (1995)

    Google Scholar 

  13. Kacprzyk, J., Wilbik, A., Zadrozny, S.: Linguistic summaries of time series via a quantifier based aggregation using the sugeno integral. In: 2006 IEEE International Conference on Fuzzy Systems, pp. 713–719. IEEE (2006)

    Google Scholar 

  14. Kacprzyk, J., Wilbik, A., Zadrożny, S.: Linguistic summarization of time series using a fuzzy quantifier driven aggregation. Fuzzy Sets Syst. 159(12), 1485–1499 (2008)

    Article  MathSciNet  Google Scholar 

  15. Kacprzyk, J., Yager, R.R., Zadrozny, S.: Fuzzy linguistic summaries of databases for an efficient business data analysis and decision support. In: Abramowicz, W., Zurada, J. (eds.) Knowledge Discovery for Business Information Systems. SECS, vol. 600, pp. 129–152. Springer, Boston (2002). https://doi.org/10.1007/0-306-46991-X_6

    Chapter  Google Scholar 

  16. Kacprzyk, J., Zadrożny, S.: Linguistic database summaries and their protoforms: towards natural language based knowledge discovery tools. Inf. Sci. 173(4), 281–304 (2005)

    Article  MathSciNet  Google Scholar 

  17. Kacprzyk, J., Wilbik, A., Zadrożny, S.: An approach to the linguistic summarization of time series using a fuzzy quantifier driven aggregation. Int. J. Intell. Syst. 25(5), 411–439 (2010)

    MATH  Google Scholar 

  18. Ng, R.: Outlier detection in personalized medicine. In: Proceedings of the ACM SIGKDD Workshop on Outlier Detection and Description, p. 7 ACM (2013)

    Google Scholar 

  19. Aggarwal, C.C.: Toward exploratory test-instance-centered diagnosis in high-dimensional classification. IEEE Trans. Knowl. Data Eng. 19(8), 1001–1015 (2007)

    Article  Google Scholar 

  20. Cramer, J.A., Shah, S.S., Battaglia, T.M., Banerji, S.N., Obando, L.A., Booksh, K.S.: Outlier detection in chemical data by fractal analysis. J. Chemom. 18(7–8), 317–326 (2004)

    Article  Google Scholar 

  21. Knorr, E.M., Ng, R.T., Tucakov, V.: Distance-based outliers: algorithms and applications. VLDB J.-Int. J. Very Large Data Bases 8(3–4), 237–253 (2000)

    Article  Google Scholar 

  22. Angiulli, F., Pizzuti, C.: Fast outlier detection in high dimensional spaces. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS, vol. 2431, pp. 15–27. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45681-3_2

    Chapter  Google Scholar 

  23. Giatrakos, N., Kotidis, Y., Deligiannakis, A., Vassalos, V., Theodoridis, Y.: In-network approximate computation of outliers with quality guarantees. Inf. Syst. 38(8), 1285–1308 (2013)

    Article  Google Scholar 

  24. Last, M., Kandel, A.: Automated detection of outliers in real-world data. In: Proceedings of the Second International Conference on Intelligent Technologies, pp. 292–301 (2001)

    Google Scholar 

  25. Guo, Q., Wu, K., Li, W.: Fault forecast and diagnosis of steam turbine based on fuzzy rough set theory. In: Second International Conference on Innovative Computing, Information and Control 2007. ICICIC 2007, p. 501. IEEE (2007)

    Google Scholar 

  26. Kacprzyk, J., Zadrozny, S.: Protoforms of linguistic database summaries as a human consistent tool for using natural language in data mining. Int. J. Softw. Sci. Comput. Intell. (IJSSCI) 1(1), 100–111 (2009)

    Article  Google Scholar 

  27. Kacprzyk, J., Yager, R.R.: Linguistic summaries of data using fuzzy logic. Int. J. General Syst. 30(2), 133–154 (2001)

    Article  MathSciNet  Google Scholar 

  28. Wilbik, A., Keller, J.M.: A fuzzy measure similarity between sets of linguistic summaries. IEEE Trans. Fuzzy Syst. 21(1), 183–189 (2013)

    Article  Google Scholar 

  29. Boriah, S., Chandola, V., Kumar, V.: Similarity measures for categorical data: a comparative evaluation. Red 30(2), 3 (2008)

    Google Scholar 

  30. Duraj, A., Niewiadomski, A., Szczepaniak, P.S.: Outlier detection using linguistically quantified statements. Int. J. Intell. Syst. 33(9), 1858–1868 (2018)

    Article  Google Scholar 

  31. Duraj, A., Niewiadomski, A., Szczepaniak, P.S.: Detection of outlier information by the use of linguistic summaries based on classic and interval-valued fuzzy sets. Int. J. Intell. Syst. 34(3), 415–438 (2019)

    Article  Google Scholar 

  32. Duraj, A.: Outlier detection in medical data using linguistic summaries. In: 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA), pp. 385–390. IEEE (2017)

    Google Scholar 

  33. Duraj, A., Szczepaniak, P.S., Ochelska-Mierzejewska, J.: Detection of outlier information using linguistic summarization. In: Flexible Query Answering Systems 2015. AISC, vol. 400, pp. 101–113. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-26154-6_8

    Chapter  Google Scholar 

  34. van Benthem, J., Ter Meulen, A.: Handbook of Logic and Language. Elsevier, Amsterdam (1996)

    MATH  Google Scholar 

  35. Benferhat, S., Dubois, D., Prade, H.: Nonmonotonic reasoning, conditional objects and possibility theory. Artif. Intell. 92(1–2), 259–276 (1997)

    Article  MathSciNet  Google Scholar 

  36. Giordano, L., Gliozzi, V., Olivetti, N., Pozzato, G.L.: A non-monotonic description logic for reasoning about typicality. Artif. Intell. 195, 165–202 (2013)

    Article  MathSciNet  Google Scholar 

  37. Schulz, K., Van Rooij, R.: Pragmatic meaning and non-monotonic reasoning: the case of exhaustive interpretation. Linguist. Philos. 29(2), 205–250 (2006). https://doi.org/10.1007/s10988-005-3760-4

    Article  Google Scholar 

  38. Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)

    Article  Google Scholar 

  39. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning-iii. Inf. Sci. 9(1), 43–80 (1975)

    Article  MathSciNet  Google Scholar 

  40. Niewiadomski, A., Duraj, A.: Detecting and recognizing outliers in datasets via linguistic information and type-2 fuzzy logic. Int. J. Fuzzy Syst. 23(3), 878–889 (2020). https://doi.org/10.1007/s40815-020-00919-5

    Article  Google Scholar 

  41. Databases: Statistic Poland. https://stat.gov.pl/en/databases/

  42. Arora, N., Kaur, P.D.: A Bolasso based consistent feature selection enabled random forest classification algorithm: an application to credit risk assessment. Appl. Soft Comput. 86, 105936 (2020)

    Article  Google Scholar 

  43. Kaur, S.: Comparative analysis of bankruptcy prediction models: An Indian perspective. CABELL’S DIRECTORY, USA 19

    Google Scholar 

  44. Altman, E.I., Iwanicz-Drozdowska, M., Laitinen, E.K., Suvas, A.: Financial distress prediction in an international context: a review and empirical analysis of Altman’s Z-score model. J. Int. Financ. Manag. Account. 28(2), 131–171 (2017)

    Article  Google Scholar 

  45. Greco, S., Matarazzo, B., Slowinski, R.: A new rough set approach to evaluation of bankruptcy risk. In: Zopounidis, C. (ed.) Operational Tools in the Management of Financial Risks, pp. 121–136. Springer, Boston (1998). https://doi.org/10.1007/978-1-4615-5495-0_8

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Information Technology, Łódź University of Technology, ul. Wólczańska 215, 90-924, Lodz, Poland

    Agnieszka Duraj & Piotr S. Szczepaniak

Authors
  1. Agnieszka Duraj
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  2. Piotr S. Szczepaniak
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Corresponding author

Correspondence to Agnieszka Duraj .

Editor information

Editors and Affiliations

  1. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  2. Ludwig-Maximilians-Universität München, Munich, Germany

    Dieter Kranzlmüller

  3. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  4. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  5. University of Amsterdam, Amsterdam, The Netherlands

    Peter M. A. Sloot

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Duraj, A., Szczepaniak, P.S. (2021). Linguistic Summaries Using Interval-Valued Fuzzy Representation of Imprecise Information - An Innovative Tool for Detecting Outliers. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12747. Springer, Cham. https://doi.org/10.1007/978-3-030-77980-1_38

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  • DOI: https://doi.org/10.1007/978-3-030-77980-1_38

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