Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Implementing Quantum Finite Automata Algorithms on Noisy Devices Read chapter Read first chapter

2021 | OriginalPaper | Chapter

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

Authors : Agnieszka Duraj, Piotr S. Szczepaniak

Published in: Computational Science – ICCS 2021

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter The Use of Type-2 Fuzzy Sets to Assess Delays in the Implementation of the Daily Operation Plan for the Operating Theatre
next chapter Combining Heterogeneous Indicators by Adopting Adaptive MCDA: Dealing with Uncertainty
Literature
1.
Shareef, D.M.A.M., Aminifar, S.A.: Uncertainty handling in big data using fuzzy logic-literature review (2021)
2.
Ross, T.J., et al.: Fuzzy Logic with Engineering Applications, vol. 2. Wiley, Hoboken (2004)MATH
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)
4.
5.
6.
Barnett, V., Lewis, T.: Outliers in Statistical Data, vol. 3. Wiley, New York (1994)MATH
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)
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)
9.
Jayakumar, G., Thomas, B.J.: A new procedure of clustering based on multivariate outlier detection. J. Data Sci. 11(1), 69–84 (2013)MathSciNetCrossRef
10.
11.
Yager, R.R.: Linguistic summaries as a tool for database discovery. In: FQAS, pp. 17–22 (1994)
12.
Yager, R.: Linguistic summaries as a tool for databases discovery. In: Workshop on Fuzzy Databases System and Information Retrieval (1995)
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)
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)MathSciNetCrossRef
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_​6CrossRef
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)MathSciNetCrossRef
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
18.
Ng, R.: Outlier detection in personalized medicine. In: Proceedings of the ACM SIGKDD Workshop on Outlier Detection and Description, p. 7 ACM (2013)
19.
Aggarwal, C.C.: Toward exploratory test-instance-centered diagnosis in high-dimensional classification. IEEE Trans. Knowl. Data Eng. 19(8), 1001–1015 (2007)CrossRef
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)CrossRef
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)CrossRef
22.
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)CrossRef
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)
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)
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)CrossRef
27.
Kacprzyk, J., Yager, R.R.: Linguistic summaries of data using fuzzy logic. Int. J. General Syst. 30(2), 133–154 (2001)MathSciNetCrossRef
28.
Wilbik, A., Keller, J.M.: A fuzzy measure similarity between sets of linguistic summaries. IEEE Trans. Fuzzy Syst. 21(1), 183–189 (2013)CrossRef
29.
Boriah, S., Chandola, V., Kumar, V.: Similarity measures for categorical data: a comparative evaluation. Red 30(2), 3 (2008)
30.
Duraj, A., Niewiadomski, A., Szczepaniak, P.S.: Outlier detection using linguistically quantified statements. Int. J. Intell. Syst. 33(9), 1858–1868 (2018)CrossRef
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)CrossRef
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)
33.
34.
van Benthem, J., Ter Meulen, A.: Handbook of Logic and Language. Elsevier, Amsterdam (1996)MATH
35.
Benferhat, S., Dubois, D., Prade, H.: Nonmonotonic reasoning, conditional objects and possibility theory. Artif. Intell. 92(1–2), 259–276 (1997)MathSciNetCrossRef
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)MathSciNetCrossRef
37.
38.
39.
Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning-iii. Inf. Sci. 9(1), 43–80 (1975)MathSciNetCrossRef
40.
41.
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)CrossRef
43.
Kaur, S.: Comparative analysis of bankruptcy prediction models: An Indian perspective. CABELL’S DIRECTORY, USA 19
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)CrossRef
45.
Metadata
Title
Linguistic Summaries Using Interval-Valued Fuzzy Representation of Imprecise Information - An Innovative Tool for Detecting Outliers
Authors
Agnieszka Duraj
Piotr S. Szczepaniak
Copyright Year
2021
Book
Computational Science – ICCS 2021

Print ISBN: 978-3-030-77979-5

Electronic ISBN: 978-3-030-77980-1

Copyright Year: 2021

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

Premium Partner

    Image Credits