Skip to main content
SpringerLink
Log in
Book cover

International Conference on ICT Innovations

ICT Innovations 2019: ICT Innovations 2019. Big Data Processing and Mining pp 83–95Cite as

Diatom Ecological Modelling with Weighted Pattern Tree Algorithm by Using Polygonal and Gaussian Membership Functions

  • Conference paper
  • First Online:

  • 599 Accesses

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1110))

Abstract

Weighted Pattern Tree (WPT) algorithm is as an extension of the Pattern Tree (PT) algorithm, which could be used for fuzzy modelling. This algorithm utilizes the similarity between two fuzzy sets in order to quantify how much a particular tree model is confident to predict a given class. The Membership Functions (MFs) play an important role in model induction and thus on the model’s performance. Therefore, this paper aims to investigate the influence of different MFs, not only by analyzing different mathematical distributions, but also to investigate the influence of the number of MFs per attribute used for fuzzification of the datasets, as well as the different settings of the algorithm in the area of diatom ecological modelling. The experimental results show that WPTs with depth 10 using polygonal MFs with high number of MFs per attribute are excellent for describing the training data, while the models that are built with low number of MFs are excellent for making predictions for unseen data. The results from this research can be used for ecological modelling of diatoms, to classify a given diatom into a particular water quality class.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Abramowitz, M.: Handbook of Mathematical Functions. Graphs, and Mathematical Tables, With Formulas, Dover Publications (1974)

    Google Scholar 

  2. García, S., Fernández, A., Luengo, J., Herrera, F.: Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power. Inf. Sci. 180, 2044–2064 (2010)

    Article  Google Scholar 

  3. Hodges, J., Lehmann, E.: Ranks methods for combination of independent experiments in analysis of variance. Annal. Math. Stat. 33, 482–497 (1962)

    Article  MathSciNet  Google Scholar 

  4. Hommel, G.: A stagewise rejective multiple test procedure based on a modified Bonferroni test. Biometrika 75, 383–386 (1988)

    Article  Google Scholar 

  5. Huang, Z., Gedeon, T.D.: Pattern trees. In: 2006 IEEE International Conference on Fuzzy Systems, pp. 1784–1791. IEEE (2006)

    Google Scholar 

  6. Huang, Z., Nikravesh, M., Azvine, B., Gedeon, T.D.: Weighted pattern trees: a case study with customer satisfaction dataset. In: Melin, P., Castillo, O., Aguilar, L.T., Kacprzyk, J., Pedrycz, W. (eds.) IFSA 2007. LNCS, vol. 4529, pp. 395–406. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72950-1_39

    Chapter  Google Scholar 

  7. Janikow, C.Z.: Fuzzy decision trees: issues and methods. IEEE Trans. Syst. Man Cybern. 28(1), 1–14 (1998)

    Article  Google Scholar 

  8. Kóczy, L.T., Vámos, T., Biró, G.: Fuzzy signatures. In: EUROFUSE-SIC, pp. 210–217 (1999)

    Google Scholar 

  9. Krammer, K., Lange-Bertalot, H.: Die Ssswasserflora von Mitteleuropa 2: Bacillariophyceae. 1 Teil, pp. 876. Gustav Fischer-Verlag, Stuttgart (1986)

    Google Scholar 

  10. Nikravesh, M., Bensafi, S.: Soft computing for perception-based decision processing and analysis: web-based BISC-DSS. In: Nikravesh, M., Zadeh, L.A., Kacprzyk, J. (eds.) Soft Computing for Information Processing and Analysis. Studies in Fuzziness and Soft Computing, vol. 164, pp. 93–188. Springer, Heidelberg (2005). https://doi.org/10.1007/3-540-32365-1_4

    Chapter  Google Scholar 

  11. Olaru, C., Wehenkel, L.: A complete fuzzy decision tree technique. Fuzzy Sets Syst. 138, 221–254 (2003)

    Article  MathSciNet  Google Scholar 

  12. Quinlan, J.R.: Decision trees and decision making. IEEE Trans. Syst. Man Cybern. 20(2), 339–346 (1990)

    Article  Google Scholar 

  13. Stroemer, E.F., Smol, J.P.: The Diatoms: Applications for the Environmental and Earth Sciences. Cambridge University Press, Cambridge (2004)

    Google Scholar 

  14. Suárez, A., Lutsko, J.F.: Globally optimal fuzzy decision trees for classification and regression. IEEE Trans. Pattern Anal. Mach. Intell. 21(12), 1297–1311 (1999)

    Article  Google Scholar 

  15. TRABOREMA Project: WP3, EC FP6-INCO project no. INCO-CT-2004-509177 (2005–2007)

    Google Scholar 

  16. Van Dam, H., Martens, A., Sinkeldam, J.: A coded checklist and ecological indicator values of freshwater diatoms from the Netherlands. Netherlands J. Aquatic Ecol. 28(1), 117–133 (1994)

    Article  Google Scholar 

  17. Van Der Werff, A., Huls, H.: Diatomeanflora van Nederland. Abcoude - De Hoef (1957, 1974)

    Google Scholar 

  18. Wang, X., Chen, B., Olan, G., Ye, F.: On the optimization of fuzzy decision trees. Fuzzy Sets Syst. 112, 117–125 (2000)

    Article  MathSciNet  Google Scholar 

  19. Yi, Y., Fober, T., Hüllermeier, E.: Fuzzy operator trees for modeling rating functions. Int. J. Comput. Intell. Appl. 8(04), 413–428 (2009)

    Google Scholar 

  20. Yuan, Y., Shaw, M.J.: Induction of fuzzy decision trees. Fuzzy Sets Syst. 69(2), 125–139 (1995)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgment

This work was partially financed by the Faculty of Computer Science and Engineering at the Ss. Cyril and Methodius University in Skopje.

Author information

Authors and Affiliations

  1. Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, Skopje, North Macedonia

    Andreja Naumoski, Georgina Mirceva & Kosta Mitreski

Authors
  1. Andreja Naumoski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Georgina Mirceva
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kosta Mitreski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andreja Naumoski .

Editor information

Editors and Affiliations

  1. Saints Cyril and Methodius University of Skopje, Skopje, North Macedonia

    Sonja Gievska

  2. Saints Cyril and Methodius University of Skopje, Skopje, North Macedonia

    Gjorgji Madjarov

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Naumoski, A., Mirceva, G., Mitreski, K. (2019). Diatom Ecological Modelling with Weighted Pattern Tree Algorithm by Using Polygonal and Gaussian Membership Functions. In: Gievska, S., Madjarov, G. (eds) ICT Innovations 2019. Big Data Processing and Mining. ICT Innovations 2019. Communications in Computer and Information Science, vol 1110. Springer, Cham. https://doi.org/10.1007/978-3-030-33110-8_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-33110-8_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33109-2

  • Online ISBN: 978-3-030-33110-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation